{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table class=\"m01-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/yy/dviz-course/blob/master/docs/m02-history/lab02.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://yyahn.com/dviz-course/m02-history/lab02/\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View on Github</a>\n",
    "  </td>\n",
    "   <td>\n",
    "    <a href=\"https://raw.githubusercontent.com/yy/dviz-course/master/docs/m02-history/lab02.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View raw on Github</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "ox6BA2ofV_3p"
   },
   "source": [
    "# Module 2: John Snow's map\n",
    "\n",
    "Follow the contents of this notebook and answer all questions (e.g. **Q1: ...**)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "msQ8IFvWV_3x"
   },
   "source": [
    "## Jupyter + Pandas = Awesomeness"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "-hLwyxsUV_3x"
   },
   "source": [
    "[Jupyter notebook (lab)](http://jupyter.org) and [Pandas](http://pandas.pydata.org) may be the two most important libraries responsible for the `Python`'s rise in data science. `Jupyter` lets you interactively explore datasets and code; `Pandas` lets you handle tabular datasets with superb speed and convenience. And they work so well together! In many cases, `Jupyter` and `Pandas` are all you need to load, clean, transform, visualize, and understand a dataset.\n",
    "\n",
    "If you are not familiar with `Pandas`, you may want to follow their official tutorial called [10 Minutes to pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html) now or in the near future. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "nTR20eiSV_3y"
   },
   "source": [
    "## Importing pandas\n",
    "\n",
    "The convention for importing `pandas` is the following"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-05-17T21:06:39.071Z",
     "iopub.status.busy": "2020-05-17T21:06:39.054Z",
     "iopub.status.idle": "2020-05-17T21:06:39.826Z",
     "shell.execute_reply": "2020-05-17T21:06:39.848Z"
    },
    "executionInfo": {
     "elapsed": 356,
     "status": "ok",
     "timestamp": 1684852724501,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "rVXgfGBcV_3y"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "SI0DKpp3V_3z"
   },
   "source": [
    "You can check the version of the library. Because pandas is fast-evolving library, you want to make sure that you have the up-to-date version of the library. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "execution": {
     "iopub.execute_input": "2020-05-17T21:06:41.885Z",
     "iopub.status.busy": "2020-05-17T21:06:41.867Z",
     "iopub.status.idle": "2020-05-17T21:06:41.919Z",
     "shell.execute_reply": "2020-05-17T21:06:41.935Z"
    },
    "executionInfo": {
     "elapsed": 575,
     "status": "ok",
     "timestamp": 1684852729059,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "BySTPnrkV_3z",
    "outputId": "f2717ef8-413e-4295-9eb9-1c0b46c9c08e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "O_VHaBeJV_30"
   },
   "source": [
    "You also need `matplotlib`, which is used by `pandas` to plot figures. The following is the most common convention to import `matplotlib` library. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-05-17T21:06:43.626Z",
     "iopub.status.busy": "2020-05-17T21:06:43.613Z",
     "iopub.status.idle": "2020-05-17T21:06:43.889Z",
     "shell.execute_reply": "2020-05-17T21:06:43.910Z"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1684852730215,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "DYL0_pF_V_31"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "6XjKV55tV_31"
   },
   "source": [
    "Let's check its version too. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "execution": {
     "iopub.execute_input": "2020-05-17T21:06:45.160Z",
     "iopub.status.busy": "2020-05-17T21:06:45.146Z",
     "iopub.status.idle": "2020-05-17T21:06:45.187Z",
     "shell.execute_reply": "2020-05-17T21:06:45.201Z"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1684852731736,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "R-tadeEdV_31",
    "outputId": "e53b2b48-806b-42d1-b997-c1fadc5d7d7d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.10.3'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.__version__"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "QMUK2TQiV_32"
   },
   "source": [
    "## Loading a CSV data file\n",
    "\n",
    "Using pandas, you can read tabular data files in [many formats and through many protocols](https://pandas.pydata.org/pandas-docs/stable/io.html). Pandas supports not only flat files such as `.csv`, but also various other formats including clipboard, Excel, JSON, HTML, Feather, Parquet, SQL, Google BigQuery, and so on. Moreover, you can pass a local file path or a URL. If it's on Amazon S3, just pass a url like `s3://path/to/file.csv`. If it's on a webpage, then just use `https://some/url.csv`. \n",
    "\n",
    "Let's load a dataset about the location of pumps in the John Snow's map. You can download the file to your computer and try to load it using the local path too. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-05-17T21:06:46.182Z",
     "iopub.status.busy": "2020-05-17T21:06:46.167Z",
     "iopub.status.idle": "2020-05-17T21:06:46.474Z",
     "shell.execute_reply": "2020-05-17T21:06:46.486Z"
    },
    "executionInfo": {
     "elapsed": 176,
     "status": "ok",
     "timestamp": 1684852732339,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "dGK4SkbHV_32"
   },
   "outputs": [],
   "source": [
    "pump_df = pd.read_csv('https://raw.githubusercontent.com/yy/dviz-course/master/data/pumps.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "ETf0JhXDV_32"
   },
   "source": [
    "`df` stands for \"[Data Frame](https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe)\", which is a fundamental data object in Pandas. You can take a look at the dataset by looking at the first few lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "execution": {
     "iopub.execute_input": "2020-05-17T21:06:47.628Z",
     "iopub.status.busy": "2020-05-17T21:06:47.618Z",
     "iopub.status.idle": "2020-05-17T21:06:47.654Z",
     "shell.execute_reply": "2020-05-17T21:06:47.667Z"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1684852732525,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "zGYO51jUV_32",
    "outputId": "db2760ce-83b1-4b0f-a1f9-e109f52d4227"
   },
   "outputs": [
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     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "pump_df.head() "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "JIYL_rpIV_33"
   },
   "source": [
    "**Q1: can you print only the first three lines?** Refer: http://pandas.pydata.org/pandas-docs/stable/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "execution": {
     "iopub.execute_input": "2020-05-17T21:06:49.405Z",
     "iopub.status.busy": "2020-05-17T21:06:49.394Z",
     "iopub.status.idle": "2020-05-17T21:06:49.428Z",
     "shell.execute_reply": "2020-05-17T21:06:49.441Z"
    },
    "executionInfo": {
     "elapsed": 151,
     "status": "ok",
     "timestamp": 1684852736102,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "_OfZbRaIV_33",
    "outputId": "13874dc9-672e-48d4-d9ec-79dfaeadb2a4"
   },
   "outputs": [
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       "           X          Y\n",
       "0   8.651201  17.891600\n",
       "1  10.984780  18.517851\n",
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "Ab0-qKj0V_33"
   },
   "source": [
    "You can also sample several rows randomly. If the data is sorted in some ways, sampling may give you a rather unbiased view of the dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1684852736959,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "fCVmeLMiV_33",
    "outputId": "ee2c2626-2e71-4103-b6e6-4ea9f0d95fc7"
   },
   "outputs": [
    {
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       "           X          Y\n",
       "1   10.98478  18.517851\n",
       "6   12.57136  11.727170\n",
       "11  16.00511   5.046838\n",
       "8   13.52146   7.958250\n",
       "7   10.66097   7.428647"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pump_df.sample(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "InRQ69SmV_34"
   },
   "source": [
    "You can also figure out the number of rows in the dataset by running "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2020-05-17T21:07:19.267Z",
     "iopub.status.busy": "2020-05-17T21:07:19.255Z",
     "iopub.status.idle": "2020-05-17T21:07:19.295Z",
     "shell.execute_reply": "2020-05-17T21:07:19.308Z"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1684852738371,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "-VPiKjlfV_34",
    "outputId": "19d6a2e8-0039-489c-9c4b-6879a47a5af2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pump_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "M-W9EAIqV_34"
   },
   "source": [
    "Note that `df.size` does not give you the number of rows. It tells you the number of elements. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1684852738371,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "l-4RoaWiV_35",
    "outputId": "f09f8409-b518-49ec-b6aa-1f6415aa2590"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pump_df.size"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "QUzqWROJV_35"
   },
   "source": [
    "You can also look into the shape of the dataset as well as what are the columns in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1684852738560,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "Sva1ViTpV_35",
    "outputId": "caad0b5a-78cd-40a6-e9fa-3aad2388b82e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pump_df.shape  # 13 rows and 2 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1684852738890,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "d79EmD74V_36",
    "outputId": "386a787c-2235-4615-8ccf-7af4c73e1a26"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['X', 'Y'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pump_df.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "ymyvkdS6V_36"
   },
   "source": [
    "You can also check out basic descriptive statistics of the whole dataset by using `describe()` method.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1684852739221,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "fyY-fYaWV_36",
    "outputId": "1200a97d-417c-487b-f3ed-abe9431e8319"
   },
   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.504677</td>\n",
       "      <td>11.963446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.376869</td>\n",
       "      <td>4.957821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.651201</td>\n",
       "      <td>5.046838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.999440</td>\n",
       "      <td>7.958250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.571360</td>\n",
       "      <td>11.727170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.879830</td>\n",
       "      <td>17.394541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18.914391</td>\n",
       "      <td>18.517851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               X          Y\n",
       "count  13.000000  13.000000\n",
       "mean   12.504677  11.963446\n",
       "std     3.376869   4.957821\n",
       "min     8.651201   5.046838\n",
       "25%     8.999440   7.958250\n",
       "50%    12.571360  11.727170\n",
       "75%    14.879830  17.394541\n",
       "max    18.914391  18.517851"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pump_df.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "4U61EDtTV_36"
   },
   "source": [
    "You can slice the data like a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1684852741180,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "RyV59MJWV_37",
    "outputId": "8d2d0cd5-f38f-491d-f2d9-56b28a996565"
   },
   "outputs": [
    {
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       "      <td>8.651201</td>\n",
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       "           X          Y\n",
       "0   8.651201  17.891600\n",
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    "pump_df[:2]"
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  {
   "cell_type": "code",
   "execution_count": 15,
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     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
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     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
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     "user_tz": 240
    },
    "id": "ER3mP87SV_37",
    "outputId": "810c34c9-758f-4855-b7d0-0eda0f126e89"
   },
   "outputs": [
    {
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       "      <td>16.00511</td>\n",
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       "      <th>12</th>\n",
       "      <td>8.99944</td>\n",
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       "           X         Y\n",
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    "pump_df[-2:]"
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  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 174
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1684852741343,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "jG8Kof38V_37",
    "outputId": "a5eeaf6c-d4fc-4e10-b75d-79304972fc6e"
   },
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "           X          Y\n",
       "1  10.984780  18.517851\n",
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       "3  14.879830  17.809919\n",
       "4   8.694768  14.905470"
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pump_df[1:5]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "Swl_9wtdV_37"
   },
   "source": [
    "or filter rows using some conditions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 237
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1684852741343,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "8oIj10ytV_37",
    "outputId": "bc4b7f7e-54a4-44b1-e344-e4946b27a5c1"
   },
   "outputs": [
    {
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       "      <th>8</th>\n",
       "      <td>13.521460</td>\n",
       "      <td>7.958250</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16.434891</td>\n",
       "      <td>9.252130</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18.914391</td>\n",
       "      <td>9.737819</td>\n",
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       "      <th>11</th>\n",
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      "text/plain": [
       "            X          Y\n",
       "2   13.378190  17.394541\n",
       "3   14.879830  17.809919\n",
       "8   13.521460   7.958250\n",
       "9   16.434891   9.252130\n",
       "10  18.914391   9.737819\n",
       "11  16.005110   5.046838"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pump_df[pump_df.X > 13]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "HqcwcbD1V_38"
   },
   "source": [
    "Now let's load another CSV file that documents the cholera deaths. The URL is https://raw.githubusercontent.com/yy/dviz-course/master/data/deaths.csv\n",
    "\n",
    "**Q2: load the death dataset and inspect it**\n",
    "\n",
    "1. **load this dataset as `death_df`.**\n",
    "2. **show the first 2 rows.**\n",
    "3. **show the total number of rows.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1684852741344,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "etpbYVYWV_38"
   },
   "outputs": [],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.588010</td>\n",
       "      <td>11.09560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.878124</td>\n",
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       "           X         Y\n",
       "0  13.588010  11.09560\n",
       "1   9.878124  12.55918"
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "578"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "kAC-DH-hV_39"
   },
   "source": [
    "## Some visualizations?\n",
    "\n",
    "Let's visualize them! Pandas actually provides [a nice visualization interface](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html) that uses [matplotlib](https://matplotlib.org) under the hood. You can do many basic plots without learning `matplotlib`. So let's try. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "executionInfo": {
     "elapsed": 367,
     "status": "ok",
     "timestamp": 1684852743183,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "ph6kECE5V_39",
    "outputId": "e4523db1-b57a-4eaa-e2c2-a413f94b9efd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "death_df.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "H3W83prDV_39"
   },
   "source": [
    "This is not what we want! When asked to plot the data, it tries to figure out what we want based on the type of the data. However, that doesn't mean that it will successfully do so!\n",
    "\n",
    "Oh by the way, depending on your environment, you may not see any plot. You may have to run the following command (usually not). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1684852744617,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "gpU22pmjV_39"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "L-fpNQYZV_3-"
   },
   "source": [
    "The commands that start with `%` is called [the magic commands](http://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-matplotlib), which are available in IPython and Jupyter. The purpose of this command is telling the IPython / Jupyter to show the plot right here instead of trying to use other external viewers. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "T5xSTa1-V_3-"
   },
   "source": [
    "Anyway, this doesn't seem like the plot we want. Instead of putting each row as a point in a 2D plane by using the X and Y as the coordinate, it just created a line chart. Let's fix it. Please take a look at [the plot method documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html). How should we change the command? Which `kind` of plot do we want to draw? \n",
    "\n",
    "Yes, we want to draw a *scatter plot* using x and y as the Cartesian coordinates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "executionInfo": {
     "elapsed": 236,
     "status": "ok",
     "timestamp": 1684852745073,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "ua7HqxqzV_3-",
    "outputId": "f8384e7c-26c3-4975-8996-68c4c974762f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='X', ylabel='Y'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "death_df.plot(x='X', y='Y', kind='scatter', label='Deaths')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "orVd0jffV_3-"
   },
   "source": [
    "I think I want to reduce the size of the dots and change the color to black. But it is difficult to find how to do that! It is sometimes quite annoying to figure out how to change how the visualization looks, especially when we use `matplotlib`. Unlike some other advanced tools, `matplotlib` does not provide a very coherent way to adjust your visualizations. That's one of the reasons why there are lots of visualization libraries that *wrap* `matplotlib`. Anyway, this is how you do it. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1684852746794,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "xJjY_vASV_3-",
    "outputId": "506e9547-11e7-4014-998d-2d637b0a1a11"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='X', ylabel='Y'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xYfP7CA8nRYsWLXIuueQSp1evXpIdcurr6wve9+tf/7q+zy9+8YuynqO5uVn/nXwF8mLfvn3O6NGj9bE/dOhQp6mpSf8szN+NGjVK/53cOnfurL+OGTMm1N/njeyTDRs2WLVvvMeGfC227fLa3GNBbvK9DfvS+1hhXoON72MeNJfRfqeamfn000/V8OHD1cyZM4ver76+XhdZ9e7dO7FtA2zmzVxIPcqwYcPKKmh0R3T8+c9/ZgRQxuolyplzJ8msRZh9GTaj5F3sU+7rjjgq9BpsfB/RVqrBzEUXXaTuvPNONWnSpIL3ee+999S3vvUtNWvWLHXooYcmun2AzSo9Qcv93REdLEiZvRFO5QQoJg1pL7Z+UqH7u91LQoLztF8D4mN0zcyBAwfUtddeq2666aaDhtYVsnfvXl005L0BQBKBig0jnMoNUEzJWgTNths2AyX3l+1P+zUgp8HMPffcow++6dOnh/6bu+66S1c/u7c+ffrEuo0A8iFMoGLLsgmmBCjVBI7vvPNO0WCRwt58MTaYkUj6/vvvV4899phq165d6L+7+eab9TAu97Z9+/ZYtxNAPrpxwgQqQSNpTO5ysiFwlC4lmfNI9q1b+9KlSxd18cUXF+1uMqmLDDkOZl599VX14Ycf6pOBmx7cunWr+va3v61OOumkgn/XqVMnPR7dewOQDXHMFhtWmCt9bwO6ePFiveaPyV1OpvJ3EUmZgezLJUuWqHnz5qndu3eH6m6yIQOFjAczUivT1NSk18pwbzKaSepnnn/++bQ3D0AGZostR9CVflCWyG1At23bZkWXk4m8gaNL9qHs02IXs8ivVIOZXbt2tQYqYsuWLfr/csB2795dT6Lkvclopp49e7aZrRFAdvmDhbALU8bFO+RXuj7c0TVBmRdqNqobWu0uNeAfVs3ijgjkpGjhwoVtJjNyb1OnTg28/4knnsikeUBOFJrc7bPPPtMTAaY1mZ93u5iMLZ7J+krtQ/ZrPjSX0X6zNhMAIxVbMyep9ZfCbJfwrw+Fg7EGEsrF2kwArB9RVKybJs3CTu92yVcmYwuHbjfEicwMgIpXrI57Ne00MzA2bpfp2G+Iq/0mmAHQpqGR6RDcotqg7AfdBQCSQDcTkJPgQ0bUrF27Vn+tpsvHO3+LjCSUeT3kFjQpGd0F+VjDyfbtRb4QzAAWBjAyB9Ppp5+uA45TTz21YOBRyfwt7oRk7qRk/qHPzKyajzWcqt1egh8kiWAGsIS7arAELsOHD1cNDQ1tfh8UeITlzbbIVPEu+VmhmW6ZWTX7azhVsr1uwF1sDh4gagQzgCWkAXFXDQ5SKPAIw5tt2blzpx6hI7c4i3uzqpI1nEzvqgu7vW4gJwG3e6zaEKzBfhQAA5ZlZtxGQmZBffTRR/VCrO3btydTYuBor2Lzz9g2sifM9jIHD6LEaCYPghlkiVuHIEwPXmxrrKPc/nL/1juSTJZzsXmfeYftP/LII/rndEuiEoxmAjJKGoPa2lp9M7Fh8BYo21wzUW2BbjmNt38kma37zN9d+eqrr6pp06bp+q5qC9SBUsjMAIilG8zLtrlo/N0lUj8kAWQSz2XrPgvzumx/TUgWmRkAxhQo21Dg6h9KLNvrrswspkyZUvU8PpKxCpoPKGgkmS37rBj/PqymQB0ohWAGQCyNl/zflnWL/N1K4oknnqh62LsbxLhzAgV1t/hHkmVl/h7Z/uXLlzMyDomgmwlA7gqU/QW6QUs0yO/CjEoq9hzu3/vlubvF9sJwJIduJgCpML1AuVBxb9A8KtXOdOydb8Yrz90tts18DHsQzGQMU4gjCTYfZ0GT2hUKXKoZUuwNkOTrmjVrjOluieL9q+QxbJv5GPYgmMkQrnqQ9+MsTANbaDZbN3ARUQRq3gBJvsoaWiZkrKJ4/yp9DNtmPoY9qJnJkKB+/7z2yyN/x5l/wrZiGZBCdRvexxg6dKhauXKlqqmpyVT9SBTvXzWPQc0MwqJmJqe46kGej7NyujAKdR95H2PdunX69cWReZLHXLt2rTrttNMSz3BF8f5V8xjMBow4kJnJGK56kNfjLOyaSKUeY8SIETqQiTrz5O6z3r176+HZUj/jVep5olzyIIr3z8RjANnC2kw5DmaAPCvVwIYZOr5nzx4dDLmZmSjmfPEGWtJtJc/hNWzYMNXQ0FC0W8z9e5lYb/fu3SW70gDbEcx4EMwA8bPhKt2/3IJMYOcGF3G/nkJLFogwtTlZXfIAKIaaGQC5Ht0UNKrJv9yCdPMUqomJuq4jaMkCCWJkuHZjY2PJIuOsLnkARIVgBkBVTJg7xBu8eIMrKbCVQtug9ZaEdCW53U5xClqyQIIYGa4dJmDK6pIHUa1vBRDMALB6dJM3eJHi3TfeeKM1uJLsiwQMEtTI/WStoKampjYrYFe7iGRYbrZHsjCVZH3K/XubJzb0BzDynhVb3wqgZgaZZEMNR5akub/99STS2An/aKGBAwfqRlGCAWkg3ftlsfaknDl3TCVF0rLt/vcxq+8ZDkbNDHLNxBqOrKukxiSqzIEEUFJ/4pLGb/bs2W2CFbFp06bWGhnZ1rizSWlmRkzo+quG7DN5XwoFMnle3wrBCGaQObafyPMgyoBTAigZDeQGNNIISjfS6tWrdYHtgAED2tTIFFuLKY7XJ10iSdd5pN31Vy15j7xz/QipdzJpfSuYhW4mZE4Uk6ehcmEmd4tjSYRCXV1xzBtTStBQammMpWYnqWPR5q5W/7ISs2bNMmJdKySLeWY8CGbyyeYTuc3CTu6WdMCZ1PHgDeTOPvvs1gyhSybGkyJlU6XxuSm2Thaf4XxroWYGecf6L+l38UkgU6irL+5unjSOB2/XkgRoCxcuVPPmzWtznzPPPPOg2X/zXGtW7Dn5DKMcBDMAIlPO5G62N1b+Al//IpUjR45U55xzTpuaHQlkFi1apEyURq0Z9W2ICsEMgMjkZXI3/9w2EqT4R1XJ6Cm5jxQnd+7cuTXAO/fcc5WJ0igaLuc5szBvDuJDzQxgEeoIzOAv8HXnsBEySZ8EMi4J6KSGRjIyEsiUWrogTSbVzGRt3hyUj5oZIAP8V6LMnxO9Sq/2g7IwklmQBlaCGu8wcbmvBDDjx483OpBJq+svzHPSHYVSCGYQO9LDla/w7M5T4l69ckKPt6so7LHrzm0jGRn/HDYSsMi6S1F3seXlcxT0Om2fNwfxI5hBrKSBkIaCbEJ55GTurvAsX+V7TujR8hfsyr71Hp+lMmEStARlYeLIcOQlK1fodSY9+g32IZhBrAvESQPhzuRJNqE6nNCjXXlZ6li8Sx7IzLLz588PHJ1U6NhNKguTZlYuyYxQsddp++g3xMzJuObmZilw1l+RjH379jmjR4/W+917GzZsmP5dEs+/YcOGks8lv1+/fr2+JbFd5ZDtGTVqlN5vsi9N2z4befep3Orq6pwVK1YcdJzKfeS+3uN4zJgxib0H3ud133uTtiWp50vydcL+9ptgBpGTQCIokPnss8+MOfn6Gza3ATNJ2KAMlR+XtbW1Oqjx/1zum8R7EPT4/u1MaluCFNqWOHHco5L2m24mRM5b2yFfZXG4//zP/0wkPRw2HS8/d2tShPzftC4w0urRH5eyPpKXdDc9+eSTbbqbvCsyx/keFKoPKVQblcbxkEadFsc9KkEwg8j71oVb2/Hqq6+qr33ta7qxSKJwMezJ19+weRsw2KGcWg53NNiSJUvU3Llz2/yuU6dOeoVtqZlJckXmQoG3SbVRaW6L+/7KIIJC73NeRnghBCfj6GZyEkuXB3XvpJ2mLva6Ta6ZQfjjbejQoW26MP3vuf/YlPuaUI9EfUi4fdOlS5fA9yrpeh4kj5oZD4KZZD70xfr5vSdsaUiS6g+X55KGzvaTHTUEpY+3gQMH6vc76FgPOjZN2adRb0c5xe8mvP5y6pv8F0NpXCghWQQzHgQzyXzoi11luidOaWySupKSx3YDGVtPdm7myIQsgmmC3l8p5JX9FRS45CEDEjZ4tyGjEZSZCTqv5OF9zbNmgpm/IphJ7kNf6movySsp/3MlNSw87uHttgVkcTfeAwYMaLN/mpqaAo910zMRSQbvtmQ0vBdBxbqKs/y+5l0zwcxfEcyY86FP8krK+1xJDQuPO83O1efB1qxZ02YfufVPaR/rSSsneM9qRiOP73vWNTM0G2GGOyYxEsD7HEmOjPA+V0NDg/EL/JUa3i6jbBYvXqxHvDBy469qa2tbR6XJfnKP8bwN7fUeL8OGDVMrVqwo+PpNGi0V1eimvCz3gCKcjMtCZiaOK44k+s1t6Js3mX9UFvsyWB6vyINec5b3Q6nRTbZ0naE8ZGZytrJvJZJY64VVnqvjzTCwLwvLWyam2GKMpuyHqLO+3uN/9+7dB30OWIQVBDOWrewrH9QoThBJfPg5weRvXzKJWfz7z/TANo4uH+/x36VLl8DZkW3sOkOEnBQtWrTIueSSS5xevXrpVFJ9fX3r7/7yl784//qv/6or9CWtKPe59tprnffeey/T3UxBE37FNcQ4ibR0VlLf3pEVaU20Z/q+LDaRnS2vIU3e9cL8a4UV6nI0sYA3ri6fMKObkC3WjGZ67rnnnFtuucV5+umnDwpmPv74Y+eCCy5wfvOb3zhvvvmms2zZMucLX/iC/pBnNZgp1Bh4548w8eRlq7ANq3f/d+7c2ejFKU1qxGTeF2ZsDc8/R458X+i8YHJQaHqwBXtYE8x4+YOZICtWrND327p1a8H77NmzR79w97Z9+3Zrghl/YyAnrijmyTD5xJeWsA1rUGaMQkOn4L7yr0At87640i7SNP1zUCiYKXZeiEu1WRDT9zXskNlg5ve//73Trl27oi/s9ttvD2x0bAhm4uhS4mo4WNiG1X+/mpqa1v+zP0vP++LNzqR5xR6mC8ykbibvsRXlecGdVbpYN2mpkUNAUjIZzMjJZ+TIkc4111xT9HFszszE0aWU9tVwteK6wgvbsPon3/vkk09ytzhlsfcgqMbLn53xHnNp1WkVWsvJNIX2TxTnBXkM73tTqJs0zLpIQBIyF8xIMfCll17qjBgxouygxKaamThO+Db3X8edVQq7nwvdLw+p9GIZjULvT5o1Xt5tksbaDTqDshtJdNdEqdqu5qDu0qAAJSgzY9u5A9mQqWBGApnLLrvMOfXUU50//elPZT+ujcFM1ExpdMOkuOPKKkW1D9zHkQxNFlbkLqVYRqPY+5PkMed9rqCsgvv+yHbL9ucx21Bsv5g6eg9ozkow4wYykhr98MMPK3pcgpnoVdJQeesBiqW4q8kqFcugRJHh8T6Ot3YmS41iULdRoYyGCVk/f+ZIgsxiC3TmdWSgfz9JbVOY107NHdJkTTAjJ57GxkZ9kw2eMWOG/r+MVpJAZsKECc4JJ5zgrF692nn//fdbb3v37g39HAQz0WdWgooUy61ZCBsAlNMVVKg7JKoMT6FaApNW5K62K6LQ1ACFMhpJZ/38zxc00sfNJrjHqT9oMSVTmbRKXrftNXewmzXBzMKFCwMbh6lTpzpbtmwJ/J3c5O/CIpiJhrehK3ZyK3Ql58/MRH2VV2pYexQZhKBaAmnkGxoajGgYq72KLrYPTchoBL2+YiN98hq0RMmE7Bvyq9mWYCYJBDPRCMpKBJ3cStVRxNX/XqxRi6tmJszIkCRVexWd1D6MKguT966jpBAUIi0EMx4EM/FcFRcKSNK8kgvbqEURVFXabRanKPa9KYFBUJdXsddHg5usOC9MgEra73byj8qwlpYW1a1bN9Xc3Ky6du2a9uZYTRaMk0XtZHG3Ygu5hb1fGtsovz/jjDPUqlWr9PejRo1Sy5cv1/d1F/kTpVYf9j+OLIInC92lvcBdFPs+zffPJe+DLFToGjp0qGpsbNT/T3vb8q7YZwiIUjntN8EMMs3fMPsbSSEr7crvyz1Be1c4lvuVCoDyoFAgFDZA2rNnj3r55ZdVr1691LXXXqvWr1/f5n2SfYx0A85CnyHem/TsN+ACJO32+5CwD7pjx44otg1I9AM+duxYfeKtra1Vu3bt0h92CVRcklGRn8mJwA1khPxfflaMG8B87WtfU3V1dfq55Dnzyru/vfui0M+DApmjjz5aXXzxxWrkyJGqY8eOer+KMWPG6PcpydfiBqqmCbs/4+L/DMn/ZRvi2g6T3wsTXlPax4MxnJCOPPJIZ9asWY5t4qyZoZ/eLKUKRmXFa7f2wt/fX+loq7wPXS02Yd2CBQsCf15oH8n9/XVI8h4l/RkzfW4VE4459zMk89WUmqohy++FCa/JhOPBqgLgmTNnOocffrhzxRVXODt37nTyHsxk8UNmc8BWaNiuf34UaTCjLGr0F6VWusqwjYpNWOddoFD2SZjCZLmfBJxpjxAzvXEwabh03PvK9PfChNdk0vFgzWimzZs3O+PGjXN69OjhzJ0718lzMJPFD5nNAVuh90MaWLeBlAY2jsUF3YDP22ibvr8q4Q/2/PtchqrLPvBnWLyZm1L7RP5+3rx5TlNTU6ojqdxsgxukmRagevdnmhcccTekWWyo43hN+yy56DRuaPaDDz7odOjQQc98Kos/em95zMxk5UOWdsBWzQey2PvhNrBxr5JcaH9lYRirvxtO/u9fhdntFrL9s+F/rW4wbGKAGvaCI87GLu6GNIsNdRZfk3XBzDvvvKOzM8cee6xz6623Oj/84Q/b3ExDzUzlkmyUosgCpf1+BM3FU87kemlvf7FtCJpXR+ol5OYPZoo9jg0KLVthYgY2zCSVkuWKs66lWKbS1mMgTjZ/NjITzPzyl790jjjiCGfSpEkVL/yYNCbNs+ODV04WyOSTpduAuI2Htwak2GsLmiQujW0vtA3+bIXbbSfBWpINZdqZGdOya0E1W24AM3LkyMQDMu/2eOumTNpnaZ4fTficO3kPZsaPH+8cddRRzuOPP+7YhGAmW1kg7wy1pp4si13ZF9rWYusiJXUS9m+DFE/7Axq5oPHeR+pbsnil6e0a9C9cadrx5g3u/QGn/xZ3hrXQsW9aRiutbHLSn3PbxRLMXHDBBc727dsd2xDM2KNUoxi0dlDSJ8swDXfQ1alsd7Gi1qDXFtdrKnQSDtoGf0DjH3HkFv1Wuh2mZthsLPgvFkRLkJNEVino2LexbiquYyLJz3kWsDaTB8FMdgTNG5Nk+r+cK7FKGuqk1kUqdhKWbfAPZ/dfPUpQVizgCcOGDFux7gGpFTJtW4OKtGU7k+4a8x/7YT8DNgW31dQUmrL+mQ0IZjwIZsxWTheF9+QhI+mkXiPJ9H8SV+dJdNmUOgkHBTT+wtJSGZwg8nuZ0qG+vt6pra21rjvCP3rLhJXS/UwbORfmAkB+JkGXu29tCG6r/bxmsWs2DgQzHgQz2epz9p8Ekkz/BwUBJpyUKtmGUn9T6uoxKOApFtD4u6eCMmxhr1LT2ucmrpRuulLTFXhnELYtuLXZPgPOW2EQzOQwmLHl4PSKIhDxB0TSBRLnVal3uGvcU7mnPToiTMDjD2jk6jro/kFLFXgzbJVk55IcZuwGrpUseZFnYaYrqDa4RXmS/gxVg2AmZ8GMTQdnFH3OhbovvF0XcXUBBE0Ul+RVZKnMVCX1K9WQ5xowYECbbZBAL+h+3syMdCVUUneSVibOu0SGd5STbRcQaQgzXYG3PsvEGZezZIMFxewugpmcBTM2HZxRZ5T8V8tx7odio6niuor0Zwb8WZigbUp6uKd/4jwJboICKjfofPbZZ6saARXU1RdFnUjYLkz5vQRsblBr0wWEqSOt4s6oIvqLyCQQzOQsmLHp4EzqJBlHAxP0XO6IkThOxv7gxT+KyA1aShXsxk22wZ+tijND5B314r3iryYj58/C+B/b/VwVyszZdAGRhqAh23KMrFixgiAmBfssKUsgmMlZMBPm4LTl4K02MyMNTVzDZoOG5/onKouyeytoIUd/Q+o2omkP9yy3IDjK96LSwML7mQiazMx/PBXKzEndT9Y+V3Fw97N0I9k0LB/pIZjJYTCTxZqasEp1M0QZyJXqiij3Kr3YtgU1nqtWrSoYtKQdsCYZ0FSbkfN/JrwNrAQnQRmwoOf0dvdl8WIhDnmaJRjVIZjxIJiJv6bG5BO5N2MRRyBXzQiXMCORguY2MbnwtJwRTtUodxRbqSDUPUZk2z/66KPAWVqDMnP+WqYsXiwk0eWUt+5xhEMw40EwE66mptKAxOQTeVBmI66J7iopQA3qRgr6e3lcm65gg0Y4ScMftbDHbKl6mKBMjDdT4/3MBD2nzQX4afHWPZkamCN9BDOWBzNxZDpKdWeUO01/qREfJvBvmzRaJp08gwpngxp9Gwu8w45wSqLRK1UP483e+TMxlQRLFLQC0SCYsTiYSSPTETYgKTQ02NSG1rtt7uRspmWR/COUCmVnTO7KCxuolTtkPKg7opL3zX8cBG1TtQXUbnYu7UkUbZbkMW7b5ymvmglm7A1m0sh0hA1Igq5w3ZoBU08MxUasmJBFCmr0TdiuKFQ7ZDzKQlFvhqdQ12O1x7GJx5ctkryIM7lrHG0RzFgczMSZ6SjV1VTqRJ5UDUpcTM0ipT2s2tTXFlehaFz729TjywZJBoIEnfYgmLE4mImzZiaKqxHbG960skilntfk7Fa1qnlt1dTMVBu8VyLL72OckgwECTqz2X63k39UhrW0tKhu3bqp5uZm1bVrV5VXGzduVIMHD279fsOGDWrQoEEH3W///v1q8+bNqn///qpDhw6BjxXmPmi7v8aOHatee+01NXr0aLVs2TL2WxFRHF/sc/skeV7hHJa99vuQxLYKqZIPrZzUxZgxY/T3hRoACXpOO+00tXbtWv0zP/nwSyDESSAcOWlKoyrkq3wfhux7CUKD3oMsCHp93mNQvlb62ivd53lg6nGV5HmFc1j2EMzkhHxo5epUMjJLly4N/BB7G4D169erU089VQc1e/bsSWGL8xVIekkj8/rrr6szzjij6kbdVPJ63NcnX93XF1UQErTP027E037+KINFwDhOxtlYM5OWoALfuNfYyYtK5izJcpGifyJA+T7qegbvPk97BEvaz++i+BVZbb/JzORMsatDydasXLlS1dXVtfn5pk2bdJYmiQyNCVevaaa1vZkJV5hsjunCvq+FMoiVHBfefZ52t1Paz19plhCwBcFMjnhTzCNGjAgMTmpqatTq1avVmjVr1IABAxINaEiBt21s5Kt09xXqFrRF0PsqQcaoUaNaX6e3GN0f+IU9LooFPGk34kk8f5iAL0x3M2AlJ+PoZio96Z0JqyAHbV9eU+A2D+8tZ+2isK+z1HEhfy/LEriTDxbqxkl7vwY9f1Tb5O3Gks+ozHYN2I55ZjwIZqqb9C4ooCl3Wvpytq+Seom0GykUrwuptg6m2N/7VxW3KRCOckV3f8BXU1NDnRusRzDjQTBT/aR31U5LX45yAxNTCitRPINSbcBZKKsRVLBu2oKiScymLY/n/4wuWLAg0m0GkkYw40Ewc7BKGhZTZ/7NY9dU2pmoQs+f9MyqQWs3yTFq4oKiYVZ0r3Y75XVLRsZd/oHMDGzHaCZEPmGUFAY3NjYaVziYdmFn0qQAW4q30yqSLlaMm3Rxqfe9Hzp0qC5al2N0x44dRowcCrvtw4YNUytWrGizvyoZvXX44Yerjz76SC1YsEDt3LlTf2aBvGA5A1gvS1OTF3st8jsJZNatW1dyWYq0l8VIc395lzKQANek4DvMe+3dfhnx9cQTTzBbLXKpheUMkCdZmZq81BBkafi8gYxc0Sc9xNe0TFjQe2/L8ONCx613TppVq1bpeZ+8syQDOBjBDGCIUhOr+btVnnrqqcQDqkoChTQmQiwUKNgwKaP3fXZJUDN//vxcL8UAFEMwAxiiVNbDDSRkIr1OnTqp4cOHR143E2am2nIyYZVOhBh14ymP09TUpNcaM31SRvd9njdvXpufT5gwIZW10pjMEjYgmAEMESbrIT+Tm1ypV1vgGhQwRN2NVMk0/lE3nu6ilhL8SSBYzrakRd7jL33pSwctLeIuAJtkQGPKUgxAMQQzgEHCZD38AUffvn3LzmIUChiirjepJDiKuvGUv3eDP2+9USX7LUmy7+X1B62VJvsyqe02rU4KCORkHPPMIIvcuV5kLpFCc6oUm48myfl5qpkIMYr5auTvR40a1WYumo8++iiy2XfjJtvW1NTkDBgwIPE5lbzHmcmTECKbmGcGyEkGZ9u2bYFZjFKLiiZ5tR20cGSxjEjU2SH5++XLl+suGrnJyvBnn31268gw07tOZPslk7R27Vpd+J1UhsR7DMmxknStDlAO5pkBLFZoThX/fDDSCMqEcv75TJKen8e7vdJAStAStpA4qm317xsJFBoaGowdwl3Nfqhmv/n3k5AuL3nvmJAPSWCeGSAnCmUxpPFyr+KFZCH82Yc05ucxoSC41Oy7Jqt0JJmMgpLRXOXsO/8xlFYBMhAGwQxguUITx0l3SpLdErYUBHsDQMnIVJplMH3uFe9+kyBERnOFnXzPzejIfgoqQJYgMGsBjenvJ4ojmAEyysT1tCqph4mjvqdYhqNUoyY/f/3113VgYPLcK0GZFRnVVSoY9GZ0xo0bpwOiuXPntrnPW2+9lakMDXPpZICTokWLFjmXXHKJ06tXL12xXF9f3+b3Bw4ccG677TanZ8+eejXY888/39m4cWNZz8FoJsB+Sa0U7h1JFTTKyft7G1Zrl1FIdXV1rdsZZsRb0Eg3+f3IkSMPet0DBw4MXJ077ZXdy5Xk6D5kcDTTp59+qlOfM2fODPz9z372M/XAAw+ohx9+WP3hD39Qhx12mBo/fnxmrgYAhJNUfU+pLi3v712mdOEVys6tXr26dSSXt+C60Ii3oEyY/I2cg6XuZsCAAYFz3rgZLXkc27IczKWTAY4h/JkZycpIRubnP/95688+/vhjp1OnTs6cOXMKPs6ePXt0FOfetm/fTmYGCMm2K+qolZrjxp+5Wb9+vTX7yv/e+rMRMu+O+7tix4FkYiQj4/3befPmtc7l487fY1KWI8xxnfdj3/bMjLHBzNtvv61/1tjY2OZ+55xzjjN9+vSCj3P77bcflAolmEFYeT6hSSNly0RyaR4DNh4jQd1ncqs08PAeK126dDnofOv+LopJD+PuOoS5rOlmKuaDDz7QX3v06NHm5/K9+7sgN998sx6T7t62b98e+7YiG5IuAjRp9IRsg6TX055IzoR9UqpLK40h7XF0n1Uz4s0tLl+wYIHavXt3m9/J48jjmlJ4HmY0nAnHHapjbDBTKVlNWCbX8d6AMCeuJBfUKzVDb9LktbqBjJCht0nXDTCiJPmakGpGvMl9zz///NbHla9SlyOPI49rSsBXqh6G4y4bjA1mevbsqb/+8Y9/bPNz+d79HVCuYieuJIsAvYGTBBFpD3M1YSK5aoNJrq4rGxIflGkKuy+9jytfa2trjQhgypkOgFXBs8HYYKZfv346aHnppZfaTG0sFfXSCAGVKHbiCjrpxdVA+ucASXol5LgmkqtGNcEkV9elhe0eK3dflnpcE4LMYtvISKaMcFL0ySef6AJfucmmzJgxQ/9/69at+vd33323c+SRRzrPPvusXjV24sSJTr9+/QLnNSiEeWZQ6YrMxQoHqykCdf9Wjn//qBATRn6kybtfy9nHzBMSnaj2pbxvMtrLHeVkcvGtjUXdedBsy2imhQsXBo48mjp1aptJ83r06KGHZMukeeV+sAhmUOmJq9DkYRJYuxORlXuC9gdIEtCYNPLDFOWOQPEHqXLBQ+MUf8Af5jEIMlGpctpvVs0GQq5I/fLLL+sp7KXI0Uu6ZiSFXclKxPK3ktZOevVq0wXtp1L72F1PqG/fvurss88ue2VuRLdKedCK295V3YEwWDUbqJC3f99bR7J48WId2PgDmXJH/RSaXdWUkR+mqKSOwd2P27Zto6CzStUek973zzvKiWMccSEzAwRkYvxX9EFXmlLAK/NpuMWyYa9mq73qjeoxTFfpa/Rn1KQRFVnfX6bJwzGKeJGZASIe6eS90pQgZs2aNXp+Dm8gE3YEiHvVKyoZ5ZGXkTuVDhn2Z9Tk/qavcJ1FZByRJIIZIETXhreBlCBG5oXxnqTLnauimoAkr/NilBswyvsntTN1dXVq1apVudtfQJ4QzAAhJ9eKcq6KcgISfzYir/NilBvE2bbCNUozYc4amIlgBoggNV4qEPILG5AEZSOSnNzPJOUGcRShZkteuldRGQqAAYMLJMMMUS5WuJz3olKKUPM9XB92owAYmZaVLESYLFCYbESeamjKzZxVs+4QzJLX7lWEQzADq+Qt1Rym+4qTfDaOH4KsaLtykS90M8EqpJqD0Z1i9/GTp65CICy6mZBZZCGCMaeH3cdPkl2FZICQRQQzsAqpZmTh+ElruH1S3WwETEgawQysQxYCNh8/YYfb25oBMrkuCdlFMAMACSoUUCQRZCWRAcrT6DqYg2AGABLkDyj69u2bWJdMEhkgU+uSkG0EMwCQIP9CmLJ+VJJdMu66VZIxieP5TKlLQr4QzABIRZ6LRN0upW3btiXeJZNETUuadUl5Pq7yjGAGQOIoEk2vSybumpY0gwmOq/wimAGQOJuKRONsnNPokokzgEo7mLDpuEK0CGYAJM6GIlFpiF9//XV1xhlnZKpLJs4AKu1gIs3iaqSL5QwApMLkJRi8ywt4mbL8gan70rvfJJhIowDY3RcSyEhxNUtE2IvlDAAYL+3J68JmGFwmZ5BMqRMxYSRTmsXVSA/BDAAU6a6Qr+vXr9fDqOMazmxz146pQaoNXZmIDsEMABTJMMhXaZyTng8mLBptc7NESA41MwBQghSRSiBTqHYm7ZqVtJ8fiAM1MwBQxogluRXLtgRlP9wh27t27VIjRoxINWtjStcOkBaCGYBZQ3MbxJx++umqrq5O32QIdqH3399lIdyi22OPPVatW7fOmJoVII8IZpB7Jo0GQfz27NmjMykSwDQ0NLT+fNWqVUUDEW/2w1t0K4/nGjZsGDUrQAoIZpB7po0GQXwkUJVuIjeT4iXdSGEDEW+3U5cuXVoDmRUrViTa1UNGEfh/BDPIPUaD5IcEqt5AZujQoWrNmjV66HU5k6p5u5127typv0qWp6amRqWRUZRMkzdDBOQNo5kARoPkhneGWjeTkmQAEucIq4EDB6qmpiZrXw/gx2gmoEyMBskHb0Yl6UxK1CTwlsySa9OmTerUU08lQ4NcIpgBkCtZCVxl+1euXKkzMt6ARrpKqaFB3hDMADGgMBNJkMySdC15AxqpCUqriJ3jHmkhmAEiRmEm0gho3C6ntIrYmeIAaSKYAWIc6i1XyaT9kURA09jYmOo6RExxgDQRzAAxF2ammfZHfqRdC8QUB0gTwQwQU2Fm2ml/IEmsUo00cbQBMab9mbsGecwO4a+YwyoZZGaAjKb9AaSLoujkEMwAABADiqKTQzADAEAMKIpODvlvAABiLIqmZiZ+7FkAAGJCUXQy6GYCAABWI5gBAABWI5gBAABWI5gBAABWI5gBAABWI5gBAABWMzqY+fzzz9Vtt92m+vXrpzp37qxOPvlkdccddyjHcdLeNAAAYAij55m555571EMPPaQef/xxVVdXp6eD/upXv6q6deumpk+fnvbmAQAAAxgdzMgy8hMnTlQXX3yx/v6kk05Sc+bMUStWrEh70wAAgCGM7mY688wz1UsvvaQ2btyov1+zZo1asmSJuuiiiwr+zd69e1VLS0ubGwAAyC6jMzPf+973dDByyimnqPbt2+samp/85Cdq8uTJBf/mrrvuUj/60Y8S3U4AAJAeozMzv/3tb9WsWbPU7NmzVUNDg66duffee/XXQm6++WbV3Nzcetu+fXui2wwAAJLVzjF4aFCfPn10dub6669v/dmdd96pnnrqKfXmm2+GegzJ7EjBsAQ2Xbt2jXFrAQBAVMppv43OzOzevVsdckjbTZTupgMHDqS2TQAAwCxG18xceumlukamb9++emh2Y2OjmjFjhpo2bVramwYAAAxhdDfTJ598oifNq6+vVx9++KHq3bu3uvrqq9UPfvAD1bFjx1CPQTcTAAD2Kaf9NjqYiQLBDAAA9slMzQwAAEApBDMAAMBqBDMAAMBqBDMAAMBqBDMAAMBqBDMAAMBqBDMAAMBqBDMAAMBqBDMAAMBqBDMAAMBqBDMx2b9/v9q4caP+CgAA4kMwEwMJYMaOHasGDx6svxLQAABMtD8jF94EMzHYvHmzeu211/T/5at8DwB5lZUGM2v2Z+jCm2AmBv3791ejR4/W/x8zZoz+HgDyKEsNZtZsztCFN8FMDDp06KCWLVumNmzYoJYuXaq/B4A8ylKDmTX9M3ThTTATEwlgBg0aRCADINey1GBmTYcMXXgTzFSJvmAAiLfB5Dxr7oX3fkPeG4KZiPqCR4wYofbs2ZP2JgFAphpMam7Mtd+g94ZgJqK+4HXr1ulUKh80AIgONTfm2mzQe0MwUwXp+62rq2v9fv369TrdBgCIBjU35upv0Htjb7WPASRlOnv2bDV8+PC0NwUAMl1zI1f90ljaXKSaNR0Mem/IzFSptrZWjRo1Sv9fIlTpFwYARIfRoebqYMh7w5FRJXkDly9fbkRkCgBAHtHyRhiZAgCA5NHNBAAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwAwAArEYwk0H79+9XGzdu1F8BAMg6gpmMkQBm7NixavDgwforAQ0AIOsIZjJm8+bN6rXXXtP/l6/yPQAAWUYwkzH9+/dXo0eP1v8fM2aM/h4AgCzrkPYGIFodOnRQy5Yt0xkZCWTkewAAsoyWLoMkgBk0aFDamwEAQCLoZgIAAFYjmAEAAFYjmAEAGI/5s2B1MPPee++pr3zlK6p79+6qc+fOatiwYa1DjwEA2cf8WbC6APijjz5SX/ziF9W4cePU/Pnz1bHHHqs2bdqkjjrqqLQ3DQCQ4vxZDHKANcHMPffco/r06aN+/etft/6sX79+qW4TACCd+bMkkGH+LFjXzTR37lx9AF955ZXquOOOUyNGjFC/+tWviv7N3r17VUtLS5sbAMD++bM2bNigli5dyvxZsCuYkVTiQw89pAYOHKief/559Y1vfENNnz5dPf744wX/5q677lLdunVrvUlmBwCQjfmzCGQQpJ3jOI4yVMeOHXVmRiJxlwQzK1eu1FF6ocyM3FySmZGAprm5WXXt2jWR7QYAANWR9luSEmHab6MzM7169VK1tbVtfjZkyBC1bdu2gn/TqVMn/aK9NwAAkF1GBzMykkn6SL1knoETTzwxtW0CAABmMTqYufHGG9Xy5cvVT3/6U/XWW2+p2bNnq1/+8pfq+uuvT3vTAACAIYwOZmQIXn19vZozZ44aOnSouuOOO9R9992nJk+enPamAQAAQxhdAJx0AREAADBDZgqAAQAASiGYAQAAViOYAQAAViOYAQAAViOYAQAAViOYAQAAVsv8il3uyHNWzwYAwB5uux1mBpnMBzOffPKJ/srq2QAA2NmOy3wzuZ4078CBA2rHjh3qiCOOUO3atQu8j7uy9vbt25lYL0Hs9+Sxz9PBfk8e+9z+fS7hiQQyvXv3Vocccki+MzOyA0444YRQ92WV7XSw35PHPk8H+z157HO793mpjIyLAmAAAGA1ghkAAGA1ghmlVKdOndTtt9+uvyI57Pfksc/TwX5PHvs8X/s88wXAAAAg28jMAAAAqxHMAAAAqxHMAAAAqxHMAAAAq+U6mPn888/Vbbfdpvr166c6d+6sTj75ZHXHHXeEWgcC4S1evFhdeumlehZHmYX5mWeeafN72d8/+MEPVK9evfT7cMEFF6hNmzaltr1Z3+f79u1T3/3ud9WwYcPUYYcdpu8zZcoUPVM24jvOvf7pn/5J3+e+++5LdBvzut/feOMNNWHCBD0BmxzzY8aMUdu2bUtle/Owz3ft2qW++c1v6glr5ZxeW1urHn744Vi3KdfBzD333KMeeugh9e///u/6YJfvf/azn6kHH3ww7U3LlE8//VQNHz5czZw5M/D3ss8feOABfbD/4Q9/0Ceb8ePHqz179iS+rXnY57t371YNDQ06kJevTz/9tNqwYYM+2SO+49xVX1+vli9frhsCxL/f3377bXXWWWepU045Rb3yyiuqqalJH/s1NTWJb2te9vm//Mu/qAULFqinnnpKt63//M//rIObuXPnxrdRTo5dfPHFzrRp09r87PLLL3cmT56c2jZlnRxy9fX1rd8fOHDA6dmzp/Pzn/+89Wcff/yx06lTJ2fOnDkpbWW293mQFStW6Ptt3bo1se3KskL7/N1333WOP/54Z926dc6JJ57o/OIXv0hl+/K036+66irnK1/5SmrblMd9XldX5/z4xz9u87ORI0c6t9xyS2zbkevMzJlnnqleeukltXHjRv39mjVr1JIlS9RFF12U9qblxpYtW9QHH3ygu5Zckgo+/fTT1bJly1Ldtjxpbm7W6eIjjzwy7U3J9KK31157rbrppptUXV1d2puTm30+b948NWjQIJ3tPe644/S5pVgXIKJpWyUL89577+kygoULF+p29ktf+pKKS66Dme9973vqy1/+sk4/HnrooWrEiBE6HTZ58uS0Ny03JJARPXr0aPNz+d79HeIl3XlSQ3P11VezIF+MpBu7Q4cOavr06WlvSm58+OGHun7j7rvvVhdeeKF64YUX1KRJk9Tll1+uFi1alPbmZdaDDz6o62SkZqZjx45630uX1DnnnBPbc2Z+1exifvvb36pZs2ap2bNn6yul1atX62BG+rKnTp2a9uYBsZNi4L//+7/XV09SP4Z4rFq1St1///26RkkyYEguMyMmTpyobrzxRv3/0047TS1dulTX6J177rkpb2F2g5nly5fr7MyJJ56oC4avv/563bZ6s/BRynUwI+leNzsjZHTH1q1b1V133UUwk5CePXvqr3/84x/1aCaXfC8nHcQfyMgx//LLL5OVidGrr76qswR9+/ZtM5ry29/+th7R9M4776S6fVl1zDHH6GyYZAm8hgwZoksKEL3PPvtMff/739eF7hdffLH+2amnnqqTBffee29swUyuu5lkVMchh7TdBe3bt2+N5hE/GRYvAY3ULrlaWlr0qKaxY8emum15CGRkCPyLL76ounfvnvYmZZrUysgoGjmhuze5SpULqueffz7tzcss6eKQYdgyWs9L6jckY4B4zi1yS7ptzXVmRsbJ/+QnP9FXS9LN1NjYqGbMmKGmTZuW9qZlivRZv/XWW22KfuVkfvTRR+t9L117d955pxo4cKAObmTYpJzoL7vsslS3O6v7XDJgV1xxhe7y+O///m+dIXDrk+T30gAg+uPcHzBKnZ4E8oMHD05ha/Oz3yVgvOqqq3S9xrhx4/SQ4d/97nd6mDbi2efSfSf7XeaYkaBR6pOeeOIJ3b7GxsmxlpYW54YbbnD69u3r1NTUOP3799dDx/bu3Zv2pmXKwoUL9fA9/23q1Kmtw7Nvu+02p0ePHnpI9vnnn+9s2LAh7c3O7D7fsmVL4O/kJn+HeI5zP4ZmJ7ffH3nkEWfAgAH6PD98+HDnmWeeSXWbs77P33//fee6665zevfurff54MGDnX/7t3/T5/q4tJN/4guVAAAA4pXrmhkAAGA/ghkAAGA1ghkAAGA1ghkAAGA1ghkAAGA1ghkAAGA1ghkAAGA1ghkAAGA1ghkAAGA1ghkAVpG1pM4880x1+eWXt/l5c3Oz6tOnj7rllltS2zYA6WA5AwDWkVWPTzvtNPWrX/1KTZ48Wf9sypQpas2aNWrlypUslgnkDMEMACs98MAD6oc//KFav369WrFihbryyit1IDN8+PC0Nw1AwghmAFhJTl3nnXeeat++vVq7dq361re+pW699da0NwtACghmAFjrzTffVEOGDFHDhg1TDQ0NqkOHDmlvEoAUUAAMwFqPPvqo6tKli9qyZYt69913094cACkhMwPASkuXLlXnnnuueuGFF9Sdd96pf/biiy+qdu3apb1pABJGZgaAdXbv3q2uu+469Y1vfEONGzdOPfLII7oI+OGHH0570wCkgMwMAOvccMMN6rnnntNDsaWbSfzHf/yH+s53vqOLgU866aS0NxFAgghmAFhl0aJF6vzzz1evvPKKOuuss9r8bvz48Wr//v10NwE5QzADAACsRs0MAACwGsEMAACwGsEMAACwGsEMAACwGsEMAACwGsEMAACwGsEMAACwGsEMAACwGsEMAACwGsEMAACwGsEMAABQNvs/uo0FC6b/dOMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "death_df.plot(x='X', y='Y', kind='scatter', label='Deaths', s=2, c='black')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "mmWz1z7AV_3_"
   },
   "source": [
    "Can we visualize both deaths and pumps? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 899
    },
    "executionInfo": {
     "elapsed": 542,
     "status": "ok",
     "timestamp": 1684852747485,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "ISvIDLRpV_3_",
    "outputId": "9da2877e-410e-4257-dc03-1b276586a7b8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='X', ylabel='Y'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "death_df.plot(x='X', y='Y', s=2, c='black', kind='scatter', label='Deaths')\n",
    "pump_df.plot(x='X', y='Y', kind='scatter', c='red', s=8, label='Pumps')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "Tm-WOGmxeVZ_"
   },
   "source": [
    "Oh well, this is not what we want! We want to overlay them to see them together, right? How can we do that? Before going into that, we probably want to understand some key components of matplotlib figures. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "I3WkLGsOV_3_"
   },
   "source": [
    "## Figure and Axes\n",
    "\n",
    "Why do we have two separate plots? The reason is that, by default, the `plot` method creates a new _figure_ instead of putting them inside a single figure. In order to avoid it, we need to either create an [Axes](https://matplotlib.org/api/axes_api.html) and tell `plot` to use that axes. What is an `axes`? See this illustration. \n",
    "\n",
    "<img src=\"https://matplotlib.org/1.5.1/_images/fig_map.png\" alt=\"figure, axes, and axis\" style=\"width: 500px;\"/>\n",
    "\n",
    "\n",
    "A figure can contain multiple axes ([link](https://matplotlib.org/gallery/subplots_axes_and_figures/subplot_demo.html#sphx-glr-gallery-subplots-axes-and-figures-subplot-demo-py)). The figure below contains two axes:\n",
    "\n",
    "![multipleaxes](https://matplotlib.org/_images/sphx_glr_subplot_demo_001.png)\n",
    "\n",
    "and an axes can contain multiple plots ([link](https://matplotlib.org/gallery/api/two_scales.html#sphx-glr-gallery-api-two-scales-py)). \n",
    "\n",
    "![multipleplots](https://matplotlib.org/_images/sphx_glr_two_scales_001.png)\n",
    "\n",
    "This tutorial [\"The Lifecycle of a Plot\"](https://matplotlib.org/stable/tutorials/lifecycle.html) provides a good overview of the relationship between the figure, axes, and axis objects, as well as a common iteration of creating a useful plot.\n",
    "\n",
    "Conveniently, when you call `plot` method, it creates an axes and returns it to you. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "executionInfo": {
     "elapsed": 166,
     "status": "ok",
     "timestamp": 1684852749490,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "LCXtKHnNV_3_",
    "outputId": "d55f0eb2-7fee-420f-b0d2-a6be709b3bbd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = death_df.plot(x='X', y='Y', s=2, c='black', kind='scatter', label='Deaths')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1684852749491,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "HW1x9mSPV_3_",
    "outputId": "1977941e-e753-4578-cf2d-e608fe4604d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='X', ylabel='Y'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "B3dp1tBFV_4A"
   },
   "source": [
    "This object contains all the information and objects in the plot we see. Whatever we want to do with this axes (e.g., changing x or y scale, overlaying other data, changing the color or size of symbols, etc.) can be done by accessing this object. \n",
    "\n",
    "Then you can pass this axes object to another plot to put both plots in the same axes. Note `ax=ax` in the second plot command. It tells the plot command _where_ to draw the points. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "executionInfo": {
     "elapsed": 351,
     "status": "ok",
     "timestamp": 1684852751467,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "C1Q8tKHLV_4A",
    "outputId": "3f46d485-8359-42f6-ea7b-006a31f4f3bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='X', ylabel='Y'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = death_df.plot(x='X', y='Y', s=2, c='black', alpha=0.5, kind='scatter', label='Deaths')\n",
    "pump_df.plot(x='X', y='Y', kind='scatter', c='red', s=8, label='Pumps', ax=ax)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "OZN7reANV_4A"
   },
   "source": [
    "Although simply invoking the `plot()` command is quick and easy when doing an exploratory data analysis, it is usually better to be formal about figure and axes objects. \n",
    "\n",
    "Here is the recommended way to create a plot. Call the `subplots()` method (see https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html) to get the figure and axes objects explicitly. \n",
    "\n",
    "As you can see below, `subplots()` creates an empty figure and returns the figure and axes object to you. Then you can fill this empty canvas with your plots. Whatever manipulation you want to make about your figure (e.g., changing the size of the figure) or axes (e.g., drawing a new plot on it) can be done with `fig` and `ax` objects. So whenever possible, use this method! \n",
    "\n",
    "**Now, can you use this method to produce the same plot just above?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "executionInfo": {
     "elapsed": 315,
     "status": "ok",
     "timestamp": 1684852751966,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "1eIf1U3-V_4A",
    "outputId": "1a9363ac-7d15-4f30-f362-e01cd35502bd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='X', ylabel='Y'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure and axes can be confusing at the beginning, but they are fundamental concepts in matplotlib and you need to understand them to be able to create more complex plots! "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "6D35-mOsV_4B"
   },
   "source": [
    "## Voronoi diagram\n",
    "\n",
    "Let's try the Voronoi diagram. You can use the `scipy.spatial.Voronoi` and `scipy.spatial.voronoi_plot_2d` from `scipy`, the *scientific python* library. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1684852753406,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "KhTMAkUXV_4B"
   },
   "outputs": [],
   "source": [
    "from scipy.spatial import Voronoi, voronoi_plot_2d"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "Lh3AfTGYV_4B"
   },
   "source": [
    "Take a look at the documentation of [Voronoi](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.spatial.Voronoi.html#scipy.spatial.Voronoi) and [voronoi_plot_2d](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.voronoi_plot_2d.html) and \n",
    "\n",
    "**Q3: produce a Voronoi diagram that shows the deaths, pumps, and voronoi cells together in the same axes.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1684852753582,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "wXwXlUiKV_4B",
    "outputId": "d8cc013e-949e-4198-c119-9e676ae063e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8.6512012, 17.8915997],\n",
       "       [10.9847803, 18.5178509],\n",
       "       [13.37819  , 17.3945408],\n",
       "       [14.8798304, 17.8099194],\n",
       "       [ 8.694768 , 14.9054699],\n",
       "       [ 8.8644161, 12.75354  ],\n",
       "       [12.5713596, 11.72717  ],\n",
       "       [10.6609697,  7.428647 ],\n",
       "       [13.5214596,  7.95825  ],\n",
       "       [16.4348907,  9.2521296],\n",
       "       [18.9143906,  9.7378187],\n",
       "       [16.0051098,  5.0468378],\n",
       "       [ 8.9994402,  5.1010232]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you'll need this\n",
    "points = pump_df.values\n",
    "points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "executionInfo": {
     "elapsed": 391,
     "status": "ok",
     "timestamp": 1684852753968,
     "user": {
      "displayName": "Vincent Wong",
      "userId": "06927694896148305320"
     },
     "user_tz": 240
    },
    "id": "M4rrcm6wV_4C",
    "outputId": "6c6b6302-a9c9-41c5-c5a3-bac6365448ba"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "aLkNmyE2V_4C"
   },
   "source": [
    "## Saving the figure\n",
    "\n",
    "You can also save your figure into PDF, PNG, etc. If you run the following, the plot will not only be displayed here, but also be saved as `foo.png`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "JzE4wyLFV_4C",
    "outputId": "c4d642c4-09b9-4ee4-8893-9db9c0c6b495"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot([1,2,3], [4,2,3])\n",
    "plt.savefig('foo.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "MLPpKe0lV_4C"
   },
   "source": [
    "**Q4: Save your Voronoi diagram. Make sure that your plot contains the scatterplot of deaths & pumps as well as the Voronoi cells**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "2Oc8emUJV_4D",
    "outputId": "6e550809-4c78-48f1-9998-c5193236d000"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IgcV8hrjRGhLeApMiLdrXkBmkZB1cfJiRV61aJd+NYsWKuT0kT0CHd6wzc+bMyZlwFSUIlcRnz54t7iDcPbhUr776aombSRTcSM5SG3DyySfv99zmzZtznqO0A4LHgmjB9cTabNfr9u3bi6Ah9CDb64GrYoaTwK6pWbNmMX+O6mvTpo34AfHz3XzzzSJ+dGLyHrg3OD9K9iEuhH5LTz/9dFZ3Qn7gqquuMpUqVdLYGSVQlcSJTVm+fLkEAO/atUusJIceeqgI+OgMvljZpk73j3VHx3oumVhIxNRXX30lbisC7/GkTJ8+3WQLT8fMnHvuubLTpFYGJ2j+/Pmy+2zUqFHcv9mzZ4+YzJw3z+MBn2uq0OKAC1iFZnZBxDCJ5LYpCHsJgfvuu092ssQHKEoQKokjXEjJPuGEE/YphnnsscdKlpOz3tSXX36Zlvck9dsZ0EsfNOJmqlSpkvMcLioqxGMNbd68uTQlzhaeFjNjxowRdUfMDPn0pKPhkjr//PPj/s3AgQOlzL69lS5d2niagHRv5sLlQsa8qGQHFmeC9AjCo66MEptrr71WuoQTvKgoQa4kXr9+ffPaa6/J7euvv5ZMp3T10MNy07lzZ/Pxxx9LgC+ubeo5kXCAdYgQEPrAff/99xIgTCCwU+iYsIsZUk2xznDwhg0bZjp27CgBT7ll1xC/YW/O1DHPEaDuzeyA7777bjErWh+qkjl+/vlnCXCtVq2amTx5cqgzl/KCnSvzAoHRmMFdJQBWWMW7lcTbtm0roRlscmjrgYjHJZUODjnkEAkMvu666yQTipgd2qTY+f9///ufvC+b2pYtW0oYyQMPPGCyRsQjMJTp06fnPN65c2ekUKFCkdmzZ+/ze+3atYs0btw44df9/fff5bW59xxr1vDB97/xvA/Zs2dPpGXLlpHly5e7PZRAs2vXrsjZZ58dOe644yLr1693ezi+uTZLly4due6669wbxNy5kcjhh//7Heeex4pr36EVK1bIvZI3U6ZMifznP/+JZPtcJLN+e9YyQ9ASNwKanKAAA1OgLWDdm3EFotQ1CDVzoPtJqST4j1IGxx9/vNtD8s21ya6S9g4ETWadAFlhFcWLuCpmqCPDpMwN1q1bJ/8nX510X8xkPXr0ED8cP6MKIQV7AhPo6EGfazrgfFG0TUk/tCjgOzBp0iTxVSuJ065dO0k5HTBgQKgyXxQlFERcZP78+WJCir61adNGfv7TTz9FbrzxxkjJkiUjhQsXjlSqVCkybNiwyD///BMMN5MF8xqupYCYPB944IFIkSJFIps3b3Z7KIECl2uBAgUivXr1cnsovmX48OGRggULRtauXZvdN+a7jWupQIF/3Uzc8zgg33m/oW4m75AuN1MB/jEBhtRsspoIBsbao2SerVu3Sr+Prl27avXVNEHgKpU3yVaYNm3afu5XJfFCneXKlZPsu6xXEydTEdcSFhnmIqywLgeMhpXdu3eLtZ9rgcJzijfPRTLrt86ISto56qijJK6D0te+qPPjccgSIHOJwpFUw1Yhk1p9DiqVUv9i/fr1oc58Uf6NQVOCcQ50VlQyAgsGtQcef/xxt4fiawiCp4otopASBaRDKqlBeQdEzeDBg7P/5uw8y5f3fVyc37HVbmmeqLiLPQepNqX8/6UDFSWN0Dx05syZ4hpR8k+XLl2kANW8efPEMqOkTtGiRaXPGwU2KTZYokQJt4ekZBmyYukrZHsPUUNFazVl3yKDkOEccC44J6mgMTNKxiGVXl0jyUNGGFaEiRMnSiaOkj6oikpcF/3EhgwZ4vZwFBdg6aP4ZLoq5Cr5AyHDhiKWmExm/VYxo2SUZ599Vsz5VHB29hBRcgdLTOPGjaV8+IgRI0xgoc4K6cnUVsqy64WeTaNGjTLfffedOeaYY7L63op3oH9RrGaMSubBtZSbRUbFjAMVM+73Dzr99NNF1FAGW8kbirrVqlVL6sjQIDGwItDl7J4tW7aI646sO+3bpCjeQ8WMAxUz7kOPDjJHPvvsM3U35QHXKc3bcM3R0A0TbGAtMjRWpRIuUxAmZopGkuWTRQsNRTnHjx8vzfGOPPLIrL2voih5o6nZiqegyR9t6OnkquRu7qYDNn78WbNmBVfIeKgi7l133SUuBpraKoriX1TMKBmnTp060mX1vffec3sonoau42+//bZ58cUXpfNsoPFIXzICDzt06GBGjhypNZEUxceomFEyDlHqc+bMcaeuh0+giNvw4cMl2PfCCy80gcdDfckQkVQG1n5iiuJfNGZGyRrEgXz00Ufm3HPPdXsonoI6MvXq1TM33XSTlNgPVb0LF7OZnNx6663mlVdekcwmCuopiuI+GjOjeBLiQHA3ffrpp24PxTMQeEoXeIoLErcRKiHjoYq49957r/n111+1YrWi+BS1zChZ4++//zaVKlUyZ5xxhsSFhJ3t27eb2rVry7W5ZMkSrXXiMljG3nrrLbN27VptPqgoHkAtM4onoV4K8Qkvv/yyWbVqlQm7y+2GG24wa9asEYuVChn3obXBpk2bzKRJk9weiqIoSaJiRskqbdq0McWLFw99MHC/fv3MjBkzzHPPPWeqV6/u9nAUY8xJJ50kqfGPPPKI+fPPP40XBTAd1LlXFGVfVMwoWQXz/UMPPWROPfVUE1ZeeOEFOQY0OrzsssvcHo4SZZ358ccfzVNPPWW85qIdNmyYWDYJEldBoyj7ojEzipJFli5dKnV3rrrqKlkwQxfw6wM4N7Th+OabbzzRSgLhMnToUHF/0cuGVhdYNo8++mi3h6YoGUVjZhTP88svv5hu3bqJ2TwsbNy40TRt2tTUqFHDTJgwQYWMR7n//vslCBgXoBcgywphhZChWnHlypW19YKiRKFiRnEN0mDDUkZ+165d5oorrhABQ6yMZst4F1yguP8GDBggLSbcBuFCBiAWmfbt25s77rhDe5wpShTqZlJcg27FTz/9tPnhhx/MYYcdZoIKX7FWrVqZ6dOnS0sHFibF2yxevFjEw/PPP2+uvvpqT7iasNAgbFTIKGHhD3UzKX6AJn/btm0LfKEysmNwWTzxxBMqZHzCWWedZRo1amQefvhhTwTbImCIkVEhoyix0W+G4honnHCCWCzoR0S2RhCZOXOmZMj06dPHtGzZ0u3hKEnQu3dv6fbOOVQUxduom0lxFXrhUAk3iLVWvvjiC2lTcNFFF0nFY91V+w96ZjGHkIWmAduKkl3UzaT4hrJly4qQwZTvBXN+OrO1Lr/8clOhQgXz5JNPqpDxcWYTvcTeeOMNt4eiKEou6AyruA6qu2rVqtK1OAhQPfbKK680O3fuNK+++qp2YfYx9evXF+saRQ4DbsRWFF+jYkZxHcyIpUqVklTYbC0YmSoNz/g7duxoPv74Y8leIi5I8S+4loid+eijj8y8efPcHo6iKHFQMaN4gp49e5rly5dL1+JMg4ChJPw999yT9tLw1M2ZOHGiGT9+vDn33HPT9rqKexDzRBYamU2KongTFTOKZ8z5NWvWlH5FmYZ6HcRB/PTTT3LP43QwZ84cKWjWvXt3c+ONN6blNRVvWGeInVm4cKHUCVIUxXuEMpuJj8wExY6c+AYbfEq1T352xBFHyO/hhqB8OM/b3ylWrJgpUqSI/IwgT/s8N96nTJkyZvfu3eazzz7b52fczj//fHlfCnKxgNr35P60004zpUuXNmvWrJHMCefflShRwlx44YUyVoJJo1+3Xbt25pBDDhG3xrfffrvP52nYsKH4/EkxnTp16j7vedxxx0mtF6C1gPNYcHvggQfM8ccfL5aG+fPn7/MzyvKTVk3GDlYV58+OOuooKTZm+9wgGpw/f+yxx8yZZ55pRo0aJWX97fOcK24tWrQwHTp0kHFnIoPEWmYQMuy4b7nllpQDdCk3T5G18847T+JkChYsmLbxKu7DNUNlYL6LiFZFUbyVzeR+F7UswQKLUGFSovLsyJEjzZIlS8zZZ5+93+/ZfkH8bPXq1fv8/PXXXzdNmjQx48aNM3379t3nZ9ddd5159tlnpQdP9OsCIoLFGeHw4Ycf7vMzRMoNN9wgE+Xtt9++z88QMtz27NmTs/A6b9QvQcwQQEvWhfNnxxxzjIgCOgFTuM0+z2JLjxcrZhgPtV6cf4soA4QXn8n5M8YC/J9mfM6fOfvGEDNStGjRfX5uq/2eeOKJIrbs8xwbxCOfgwJz5cuXF8F0/fXXm4oVK5p0wXvdeuutaauoyuuQuVSyZEk5xipkggfXCNYZqgETD4VwVRTFO4TGMjN8+HDJKmFSIhUYsbFlyxbz5ptv7rPQ0jOHhQneeecd6aljF3/u2Z0hEL7//nsRCM6/pUInCzQWDnbq0aKDNF0W7J9//nkf4cBrs+Dz3ogEbvb97I0mc2EBwfnuu++aZ555xrz00ktyDqnIirBhMcE65hU4jxdffLFY07C4cY6VYMJmhLmDczxr1iy3h6MogeePJCwzoREzWjTPn2Admj17tggbrGIIncaNG5vWrVuL6MQi5SZY2caOHSsWNeJ+lGBDLzEsqLgocQ0ripI5VMw4UDETHHD/UUkXYbNo0SJxV1HPBYsNlVqz7d4h3ufmm282jz76qLntttuy+t6Ke5Y43LOnnHKKuEO1AaSiZA4VMw5UzAQTAqWJT0HYrFq1SoKZiVlC2LDQZLr0PG6wBg0aSKAyYkYJD5MmTTLt27eXIP/3339frDSnn366xGGpoFGU9KFixoGKmWDD5Uu8CqKGbC0yzKpVq5YTOEyGWLpZt26dxPCcfPLJUhcnTPFMyr8Vnk866STJyMMiQ7YeYpru6MTNKYoSst5M7G4vu+wyyQJhJz1jxoz9fmflypUSG8EHIoCXWiQ//PCDK+NVvAfXDdcEad4bNmwwr732mqlRo4Z58MEHJZMK9xM76d9++y0t77dt27ac65HgZBUy4eOggw6SgovMV5QuQMiQ4u/M4lMUJbu4KmZ27NghLgHSnOO5EmrXri0+6gULFpjPP/9cSouT9aMo0SAsyCzC/bRp0yZJd+c54lqoD0L9mpkzZ8rOOj8QH4HFh0w2asmEaReeqfYPfqVt27amePHi0vUdi0w6ahUpipJ/PONmYodN0bcrrrgi57lrrrlGFiMyCBLFpjY7zVS4GtTNFF6okUMRP64jWiZQS4gUb4RJMoX5evXqZQYNGiTZVYimsOAsMqixIf8falVR7ZlCleXKlXN7OIoSOHzjZsprAsVlQLE0UnGpLUKhqliuKCeUw+fD21smYiYUf4Eb88477zTLli2TisVYahAkVOulLlCfPn2kLlBuUAyRa2vIkCGhEjKZbP/gd7iOEMYIXEVR3MWzYmbz5s1m+/btMlHQ6I06Hs2aNTPNmzeXHinxoLQ+Ks7eKGynKBaKniFKcA/guiQjafTo0eLKJKiX/3PtOaEYHi0j6LeEKAobxIJgkdHYkH2hxhEVtKdMmaLzjKK4jGfdTLgGCK679tprJQbCQvAlgcBkriSCZjMp+S3MhxuKjBV6auFGoD/VwQcfbMKI1lOJHxBetmxZKQtAx3RFUdJHINxMtAyg50/VqlX3eb5KlSqazaSkFQLKaYiJCxNXChV9+fKwQOHapF8UQjusQgZsuw4VMvtCGxKqQFNAkTYliqK4wwFeTn8k5TY6loECaXSmVpRMwIJNgCvF0Mimo8Lw1q1bpU+XosSic+fOInSHDh3q9lAUJbS42jWbmBhnV2qKkdlsE2qE9OjRQ7JOMPNTL4SmkDR4I9ZBUTINXbvHjx8vlhn68bALv/TSS90eluIxjjjiCBE0I0aMkPozxx57rNtDUpTQ4WrMDKIEkRJNmzZtzBNPPCH/nzx5sgRsrl+/3lSqVMk88MADpmnTpgm/h8bMKOnox9OyZUvzxhtviKCuW7eu20NSPMaWLVskdqZLly5mwIABbg9HUQKBtjNwoGJGSQfULsIq8/HHH4vLicBgJVikGuR89913Sz0eiipqxpeipE4gAoAVxUsQE0EQMH2fKBWwYsUKt4ekZKAwIG4i7vNT6Zi0fVySpPcripJdVMwoSoIcdthhkrpNEb4LL7xQYryUYJCOwoC0zKCQHpWB2VEqipI9VMwoShLgPqCAIwXTEDQsfor/e0ylqzAgrqadO3fG7TenKEpm0JgZRckHVBCmCSqLHhWpycBT/N1jKl2FAW+77Tbz8ssvyzVCgU9FUfKHxswoSoYhc+Xtt9+WQmn0aqLMgOJvV1K6CgMSd/Pbb79JWr+iKNlBxYyi5BOqUZOqTTAwbThoi6B4l2z1mELotm7dWpqS7tq1KyPvoSjKvqibSVFS5N1335VeTtxwL9CGQ/FmX6hs9ZiiGCh1sUaNGmU6deqUsfdRlCDzh7qZFCV7UKH6lVdeMa+99ppp27ZtvtJ6lcylTLvRY6pChQrSJPeRRx4xf/75Z0bfS1GCSjLfHRUzipIGiJt5+umnpfM2jQcDbvD0Vcq0W/Tq1cts2LDBPPnkk24PRVF8A42kiTdr1qyZKVeuXMJ/p2JGUdLENddcI9aDMWPGmL59+7o9nECQrTiXTFC1alVpVEo7ForpKYoSu7q6rcs0duxYaSTdsWNHaRFyxx13mETRmBmPky0fv5I+Bg8eLG6RYcOGSVVYJbzfgc8++8yceuqp0muOnnOKohizdu3anF53tIfp2bOnuf/++yXWjGbTDRs2lAau2pspIGImr7oYirddDOzIJ06caNq1a+f2cBQXufzyy80333wjWW8FCxZ0eziKknXI6kNqUGy0X79+0jCaRAlqdTVp0kSyQStWrLjf32kAcEDwc7xA2Onfv78UT6O8PRlOyVakVYJD7969zapVq8xLL73k9lAUJWt8++230qeMeEKC7okphObNm0ufO+a9+fPnS9XsWEImWdQy4xPLDPECt9xyi1pmfHb+qDfCIjZr1ixJ3Y7+uVrewgHNSdevX28+//xzPcdKINm5c6dYHmnK27lzZ4l/KVSokGR7cv0jYsqXL5/Ua6qbKSBixu/xAoqRwE++xPPmzZOeTphVLexMiK3B8kaAK2m87GCyiV5f2eGDDz6Qcz9t2jTJ0lAUvxOJRMzXX38tcS/Ev1Bva+rUqXJ9f/jhhxLAW69ePWnQm19UzPhVzFBBduNGY0qWNKZwYbdHo6TRX4xfmMC2BQsWSECoFyxvahnKLkzszEOffPKJKVCggNvDUZSk2bZtm8S9YIGhjtLzzz8vlpgLLrhArC9XXXWVKVWqlEkXKmb8KGbmzcOZyICNYZzTphnToIHbo1LSeB02aNDAfP/99+b999/P8RG7aRnxgmUoTMydO1c6rb/66qvmsssuc3s4ipInyIOvvvpKLC/cmLvIPsLKiLWZonZ169YVgZMJNADYb2CRQchs2/bvY+55rL1+AgNfRCaDY489VtIOKQyVqYq0iQYV+7mGi9/gXJDRVKxYMdOlSxezd+9et4ekKHEFhLVxEOd38sknS92sIkWKSLkJqlsDmzMszpkSMsmilhkvsHatMSeeuP/za9YYk2TAlJIeMmUxoSIsu5qDDjrIvPfee7K4ZcJ1hCuD3kDUucmtV1Q2LUNhjs+xVjACgJcsWSLtL4ilUhS3iUQiUg/Jxr4sWrRIrDFYj7EiFi5c2NSpU0fEjJfXb+2I5wWIkeFEYZFBW+JPL1r03+eVrOMUBJUrV5YqlOlqHnn88ceLuwFBw66H1ESKQ6ULxALj/vjjj8UkTGzGXXfdFVc8WMtQpgl7fI61grFwbNq0SRpQqphR3GL79u05gblYZZctW2YOPfRQsbaQTn3MMcfk1EjyCypmvADBvsTI2JgZhAyPNQjYFetAtCBgAerevXvaFt8TTzzRvP3225KyeMkll0jvnhNOOEGsNanC58Iiw7hJiyTbgM/jdixMrJpJbo8pm3DtIOD43GR9IGS45xpQlGzMe59++mmO9QXrIN9FvoPUecH9zQaLYF6/omLGKxDsu2mTZjNl+Av96KOPmo8++sicffbZ5vbbb48pUBAEWGSsICDWId2Lb7Vq1eT92QWddNJJ8hxmVCYVbuyM8vo/O6vorBg+D64lnkfInHnmmZ6IhbGWCZu55YUxZRtrBaPaaY0aNcxDDz0kolZRMsGOHTvE2kJ8Fhsokg+KFi0qQejjxo3L2TzRUy4IaMyMEhp++eUX07RpUwm+Jeh19uzZpnjx4jF/9++//zbDhw8XIYMgSGfa9ObNm6Uy8MyZM831118vdRkw+zI+e6NGg/P/XL/RsIuKJ3SOOuooCcwrW7asxOXY59wsp5+NmBkb/AzpDqxOJxRSbNmypdTjQFgrSqrs3btXLMo284j2GcwfzBOTJ08WQXPuuefKBs0vaGq2AxUzioUvNj5gLBbsSrBg9OjRI+6Cl4nFFwHToUMH+f+ECRNEXOWGHQPBdzRnQ2SxWEcLnujH/E50xgzWGgSNFTyJWIAI/vOb5e25556TxwhFWkp4UdBwbqpXry4LDKJaUfJbdZdNy44dO0y5cuXku89616hRI6n7Qi0YNwJ304UGACtKDKyJHxHBIk2/nNzcR+kMjqXYVLdu3WSHhKBiDHllMjkDka3JGBdNIsGz/O1vv/2Wp+jB9Gz/T3G/aHBlOUVOXgKIicetgnCcS1yIP/74ozzG6oEJ3YuxOVjI7rvvPml3YYOiFSUv2MwsXrw4x/pCMDmW5kMPPdT06dNHCnJi6UtXwoKfCN8nVkILAoDMHhZb6z7KRuwGKdht2rQRwTBp0iRz0003JbTg26BZJituBAnzd4nE7/BZscJwIyA40V1ePDeX/T/N4xAJ/D9W41NM2IiaRAUQnyNdEy/nkokcCxbHCZO6l2NzEFp0EH744YelzYGixGL37t2y+dq4caNY8/je8b0mG9LWLDrwwANNp06dTJhRN5MSOrJV72TPnj2yWxoyZIg577zzJGspmUZrsSwz6Y7fSbXv1NatW/MUQM7H7Cyj4TxEi5zcBFBuRbr8EjNjwVLXrl0788UXX8hCpSh8r7AwWusLwpxNDUs1cwkZcDVr1nQ1/i1baMyMAxUzihtQHA0XwsqVK2XnjUUoP5OPFV72GvZzwTmmGj5DPNETSwARGB0NMQB5CR7nY+r4ePWYUQ6ebDasSDTp8zuI1XXr1kn8htPilugGIqyFFdn4EKhL0C7XAt8Trl+sLxdffLHEvoSxn9cfKmb+PypmlGyCyZcsqPvvv1/cO08//bQ55ZRTTFDJ9OJDHA+iJhEBxP+xykRPaYhIrDSJCiBu6aj5kyiPPfaY6dixowjfRF2CXjzXCJlWrVqJFQGBRpVj3CPxCiZGv16YCisiYumkbq0vJUuWNG+99ZZYZeiRhoghPi6onz9RNABYUVyAHSmxMdSnocgedUT8XIQqrwUslcUnURGEFaZ06dJyS1RMWtdXbqJn9erVOY9ZWKJhAk2k1o99HKvmT6IQQ4X1bsCAAeKK9CKJtMng+rcxXvz/yiuvlOw9FqLogomc9+gq27F+z4vB2/mF6wyRjIgh0wirI6Uh+L+ttEvMGRshJXlUzChKimAJmDJliunatatMvgsWLAhEZde8FrBEq/pmcweOFcaKjETPnbPGTzwBhNWEir38n91iNIjWRIsdcuNYWLcj1guqsOKKpKFfMnFV2cJWxSaWg+ubY0Y8GNeDPb9lypQRiwxChvNJtiD/J/08umBirCrbXF9BKqxI4C7B/1heqLx72mmnmWeffdZUrVrV9OzZU5o0YrUNu/UlXaiYUZQ0FcBjhz1y5MjAuDPz6vOUSFXfWMLFS60N+ExUReWWqIggvsHW+okngGgoSvM++1x0B3ObbWaFDseOXXvbtm1lZ06AJ9Yhr7gAeQ2ECo0HETLU8yEQGwvk448/nnN+KQbYokULETIEqxM742zlYMcSq8o2lpno3/MbuIn4PNQOuvrqqyVDkH5sWF9sLy4+W69evdweauBQMaMo+YSJnQJ4LBrTp0+XGjbpwCsZOXn1eYq1SEUvoLGES7pbG2Q7aBQrDDEO3BIdn635k5sAIvV+zZo1Um4ekcViX6tWrZzbySefnHAaezqbpdrjiwv1hRdekI7KWKeIiaF6tT2/vBdj57tA/SJnEHB0zSYeMyYsMs4yCdlqfJrOmK6FCxfmWF8I1h0xYoS0q8DKhvWFLLUwBu9mGw0AVpQUCuBddtllUgAvXluE/IiY559/PiezJa8qtvZvuLcLQToXdII6mZxtn6fc0sJjWWHAPodwsX+fLgEStKBRPg+LO9YweyMzjlggLCEcQ6fAKVWqVMyFkmsC1xV/j7WA9O/8NEt1Hl8KsnHtE+DOdVGlShURLoia/BR2tK/vJ0sMy6Wt60JwP1ZZ3EnEdCFcsMbUr1/f7WEGBg0AVpQMgZXihhtukJ00IoZFIh27LrtoLFq0SBazn3/+Oc8qtvwNmTDPPPOM/H6JEiUkmySdJfyZtHEtJbLgxHMfxXIdpGsH7iWXVSzyk5KMSOB24403ys9wVfDZiFdBnGAdGTp0qPyMHmMUCrTiBsFJMHK6mqU6jy8Q1M7iTfFEaifhJuP8YlEaNGiQXIfJnAc/WGIQafPnz8+xvvCdx02EeCZwGxHD+VLri7u4KoUJqGNni7mWC2HGjBlxf5cvDL9DTIKiZBviJO655x4J7OV6JR6iffv2aZvA7KJBJg4CAmGC2yG3Krb8DWKH7BFidzDt8zhWZd5USNTiY91HLLBO91EmLEZ5vacXsAKV64b76LiZRH8Pi0zt2rXFskJMCuebarBYRXD9cL4RGfXq1ZNdLC4OLGD8/5JLLpH4m/xWu3YeXwJYqVS8fv16sdJYKxs3gnw5/l48D/m1vsCoUaMktol1CjFD7MsFF1yQ0/keoU9ArwoZ9znQbcVLNDdBbzY4KhZ8admVJOqjVpR0QnVWLB5ktAwcOFAWlXRX37SLBrAAXXXVVXmKAP7mnHPOkV0xcRwIIDdL+MeKoXHjPb3iukjUapQf6xKigRgtG6fF4kvBNad7itYZLMy4fhC6iCBrweHvcytyF318cVshtAcPHizWFxZvTP+5xU/5Cdxn77zzTo71hSBsNiscKz4z1hcCoFW0eBdXxQwXCLfcICugc+fOUlCIST6RHTQ3S6w0SiWYMHFTywGRTMZFMvfxfmYnbHbHNHhjR5oJ8rMY8Du4lFq2bJmxmJlkyYbbIFqsON/TSzE0iQY6pyMgGnFNgDA3FmG7QC9dujRH3FDDBlcQYPU766yzxF2ESOH/xGrFCxLGImTjYhDNscbpB5eRc67gWuG4PfDAA6Z///4SW4RgoeYLVijAhcdN8T6ejpnhYqMkfI8ePcSklwjsnLk4FW9PJMQBpENsRN9b83BuUIiN3SqxBdH3TNTOxy+//LK8NotBbj2B0kF+FgP+JtGaKkEgL7HipRiaRAVqpqwapJvjeuJmv3dYZ+bNmycZSWThEFzM83Qax0pet27dHOtNxYoV98lIs3FZ9957r7iV/GZ9YWMyd+7cHOsL6eUIF6ybBDWzseZzKf7E02KGss7sFOgMmigUI6L4ktMyk2j1UGVfMEEnKiSSESYImbyS6DDnxhIb9p4FPN7PcrtHkCTqIlq2bJkE+D311FMZFzJ+J1uunbzEitPKwe6acVmrlRskKlCzYdXgO/faa6/lFEEkOJjNIgs83wlclQS6ElQO9LQi3gZhQw0cji2xN34RMnaOYS4h+3DcuHEypxEYjTXTCpdGjRrJTfE3nhUzfOEIvmJSSsZPyRfS7yXk8+NaSYfIiP4bp7suHmRKRAsG+38mP4RkIiIj+jmqorrtn8b0zIRHkzcltVL36SLaJUOQK/EcTpeTjfF48cUXZXPjtrvJy0UQR48eLW584m343uOiIlvH/h43zi21cIAAc6raWusNgtFL8y0us7ffflssL9wIWMbyQuA+IoYA3rJly7o9TCVMYoYvDBka+HYtuBCIHiej6bvvvjN+m/ApsJROsZEu1wrBgImKjOifZbMhXzbBDE/9jIkTJ2ZsYQ6KJSavSsHpxOmSwY2Ce4C0Y2dtE3vDsuYFd5NXLGOxiiBybRMvQ5C7fY4NEsXvuAGPCRJ2Bhfz3bC9hogjc9a+YQOQrY2I0+pGyQTaBfAcheqwOlkXbG4JJkow8OwszYXYsGHDfZ6jkyjPUzY+066V/IiNvH6WF4m4VhIRGam4VpT/b5VBSHO9KbnHrVA4LLdKwenGlsMfNmyYZOzwnuB8z3RXGfYyiQY98xxWM+YZWwSR4xX9XPSx4me0euBmrZQIGcoT2No3xKGMGTNGfsZrOsUNYimdxx8r0Zw5c8TyQmIItZkQUA0aNDB16tQR64uGFoQPV8UMCz3day2o/+XLl0tePwtJ9GTIpEUAGhNnslANE6GSlyDJr2vF3ifjWokWJl5wrShG+spQmGzs2LG+tzxlIpYlOm6FwMq8FsR0wxh4L76LZKHgQnC+p99ThZMhmaDnWEUQuSVaGNHC94J4Gm64qQDXHhl/iBtEDhZ0W/OIYGIrbsgOohaOFaGxrlnb34p6OvaeG99NrPZYi2xZD+vmouaOEl5cFTOkDdpIe7CBu1yUTzzxRFrfi8h9/OuJuFZys34E2bWi/MuAAQNENGfSApgNwZKpNOVYVo/8LIipjoH3Btt7KPo9/ZQqnArJWqFiHZd0HCv+3lluA8FB6rfTPUWbDjaVbNwYM+4g5lOnYEGU8TvOsdksQ16P9HLqPmndMcWJ9mZSFAdr166VXSQujK5du2bsfVJtJunsyUQNECya0YKFn1NVlsUB8U52YLoWdy8UpsvvGLww9nSPy6ufKRriBoll+uCDDyS7CJdRmTJlpMcU3aURKNH3xYoVE4sSlnP+37t3b0kPV4LPH9qbSVHyh13w6YadKfHCl5PmjTNnzpTH1113nbn99tsTXoRsTyaCHXGxMNHb3Xi24ka8YPWIN4bcFnYvFdVLZVy5FQ70MiQiUDMMQYLIplkjadKJgFWctgJYd1TMKNGomFGU/4PCYVOmTJHaMumsK+MUH8DukuwR0kgxnRNfQGBloouR7cnEeAETPHFm0YIlTHEjfiyq5yTRcVlRTExXLGuc18GddPHFF0vxPoJ4KdKXDHxPaOFAKjk9kRTFomJGUf4PerCQ7kuLgHTiFB8sRps2bRJrCgGQuH+og5GM1cT2ZMIlZi07V199dUx3lV927G4U1fNSllMi43J2VufcU9QOvCLIEil3YONpyHxLtKq7EzKVsGwi5rTSu+JExYyiGCOL34QJE6TBHIImnUSLD5vtRlbejTfeKCn3yeysnT2ZIL/9mPwSZ5HMOPMSBdHWKiBzBtzsa5WIFc0KNZshxDVEjRe3qxwnmoCBRYUs1ddff13iYfIDmUvNmjUTV1O/fv00+1PJQQOAFcUYycShZgndhble0o0zZobFiuvRLRFhU1/JGCTV1VlwzsvVhW3WUl5FDBMVP/we/Xmee+45eXz99deLSPTicYg+FpSSoHYUWT+Iby+fQyoxU6+JGjAU20v1+4V7ippjHAfbaV4JJhoArChJwMLOIoGgyYSQidUQ0i23gI3fQcgg3OyYvOSqyK26MHuv7t2757poR7vW4mWO8drEK9nYI1yB11xzjWeOQzzrzZo1ayQ9mbgTblg7sFB46RxaCHSn5AZCcfLkyWkpa1G/fn1zzDHHSJdvFTOKxXsyXlGyDCXxWSgymYrtFWz8Dh2QyYSiSGR0wTkvWB9IKececcn4GCsxRrQusG6WRMByMWTIEOmO3LRpU7HE8B7AZ6aAG24/BMG5557rmeMQD65Tqt1iiWHM9Ebi3kvxP8AxxoqGkCHziGat6arPhWWuRYsWImbsuVQUtcwooYaMIir9khrttV1tJoiO32GBj1Vwzk1LTHR1YcaHRQYhk0x1YV4XywCxUFhmiDFxZo7xmTnvBE+7HTOT3/gaa4K3x8TZdNMtdu/eLX2SXn75ZfludezYMe3vwTnEwogwP++889L++or/UDETIPwS0Okl6BpsG5gGkVj1SNIRPJzJPk+xqgvjWkr22rYtDwgajZc5Fu3+88v32elK494r9XMYL4G+tDUgPoZg3UyAgCGIGOuMihkFVMwEBK9MZn4LLhs1apS55ZZbpPZLWK6JbC/guS3KsSwxTquD8++StZw5Wx6QOUablGQzx9ysUpzM99kL9XOI3yH1GhfmvHnzxG2XKTgWWNSeeeYZsb5pZ3tFr4CA4IXJzG9gAqe8eo8ePUwQ8cI1kdeiHK/PE/c2cwchQuxFsgtWvHRnKzCcLppMCJxUNhjJnju36+fQQZtieMTFUAcnP82A8+NqIt5twYIFpmHDhhl/P8XbqJgJCG5PZn6D1FYmwnbt2gW2YZ0Xrom8FuV4giM6i4lsHVyByYoOa9Gx6egEBBOMSvzNzp07JY4mt7TmVHpopSIm89M80q1qz1hhcCeddNJJ5rXXXpOK1NmA40IwNK4mFTOKipmAEMbS9anAjpldOVkzQcUL10ReizLiYt26daZcuXL7WWzY3SNkiHch9iW/liVbT4Z2EnRlxhrH6xNUTJPDeGnNqdahSUVM5ufcuVHtmWNKd3nSpWl4mu6Ck7nBeSOVnoaV3IiNUsKLipkAEbbS9fmFxWzo0KESQ0Faa1iuCTcCxO2ibK0b0UKmVatWsthT2p4S9TZ9l7/DtcSChZBJJospGltPhro6CFjEEe9do0aNmD2tov8uv3VoUhWTXv4+IwRpytqzZ08RM+PHj5fjmm1wNfXv318K6dGEUgkvKmaU0DFx4kSzZcsWmYjDghsB4s7YFHbtuI1wRbD4EYiLRca6YQgapZXEgAEDcmJjuMe1lKoAs/VkKDZHX6zixYuLW6Rbt25m27ZtcV/b/h1p7Iiq/NSh8bIgya8Ljey/Ll26iNWqT58+rrYVQARXr15dXE0qZsKNtjNQQgVF4vCzYxYndiIssGjhUkM4kKLMrjqTi6x10WDZOPnkkyVGhfgX4lZw7Vx11VVSpJCdNTEXgMggNTs/sTGJjIdjYHsYJbp4J7rg+6EsgvMY2DYD9DiyzUqpuZPX2LFq8ruzZs2SOi8dOnQwboNlZuDAgWbz5s1p7Xav+Gv99ua3TlESnJCThTL+xE306tXLhAkbv4GQyUYwMOeIWBMaDM6YMcOUKlVKhOSff/5pvv32W7ESjRw5UhZTrEQIGWIebGxMJgQM1iDeJ5n0bPt3uf1NdNViL1alteKSIonU2rn00kvNk08+KenUuNEQnXkdd45lgwYNxKUzc+ZMTwgZwPW3Y8cOM3v2bLeHoriIupmU0LhKKIlPTxsKxlEiP0wkG7+RTkuDLdRH5hDuJtuS4NVXX5VsMlxLiIVUY2Oix59qarefUuCTiRv67bffRFiSxYe4JdYluphgNLgEL7roInmd+fPnm7POOst4BSytNWvWFFeTLQaphA8VM35h925jNm40hjTiwoVNGEl10aDA1nfffSeLaBhxpimzy45XZyUd8TW8D9k/BM0Sa4JFhHo+9Eii1D0xMjbOIl2xMU6cqd3UIWHn3rt374wIGi+kwOeFM/4HCxjp07iL6HGUl9uN43jJJZdIphI1ZCpUqGC8BtYZrK1cz5lqFqt4G42Z8QPEFDRvzocxhs8wbZoxDRqYsOFcZFk0qNyb6MJH9kqVKlUkfmMaxy+kOC0WLPCx6qykK74mlnXHdu22IifdgchO1xIuxWHDhklNIT4n1iECVjMhaJKxZGUrvib6ffITN/Tmm29KfBOBtrhxvNr6YcOGDdIwdMqUKZKlqIRv/VbLjB8sMgiZbdv+fcw9jzdtCp2FJpVUV9J+V69eLaboMGOtW8RKcCM1PbrOSrosDbEyeazLiZ10OhZz54INtp4M4hUrEP17cGsh3Hge60ImAowTzVrKVlZZvPdJRowgDIiLoUUB3xsEoVfhPNepU0fGqWImnKiY8Tq4lrDIWDCk8Zjny5c3YSM/qa5M7GQ8UG7d9uoJK1aoYJDlOFrLTHTzxUwW20tXurINasXKg8WNlG/+T+wNgcaAVYHuzevXrzeFCxdOqfien+JrEnmfeBYiro2HHnrI9O3bV6yftP3wQ+8jBHLnzp0lY86rFiQlc3j/Cg07xMhgXsMig5AhzoAqmwEtwZ8JcCutXLnSTJ482bUxeCV11ylUcutN5EZ9lGSPkc2Y4ty+/fbbsghTc4T/A4Gt7du3lxsup1WrVqUtwNjr8TV5vU88yw0WLVK0J0yYYB5++GGJQ3GrhkyyIFwRM3Tr5vMo4ULFjNfBlUSMh42ZQcjw2IMuJq8s2E5Y4JiU6d1CAGS2iC7T76WO5k6h4pWsG2csDZk1zrYBuV1XZKhZK8yXX34p/bb4nc8//9ycd955OSnVBB/nt3t1Oq/pbLWYiH4fK/7se8ay3GC5ohP1W2+9JeLPb+4azjXfc1xNKmbCh4oZP0CwLzEyHsxmspM9sQjUDSE+IbfGfdmG4l509KXeSbawZfqXLVtmTjvtNDNq1CjPp+66gVMosNAS00Icz4oVK0zz5s0l+Di3GBOOIQsuxQ9xgxBQzIIWS7jk1z2ZCREaayyZ2Ag4s9eiP0e05QZRSGwMVi6aRTZq1Mj4EVxNbdu2lYBg4miU8KBixi8gYDwWI+PMjNm6dav56quvcnrruLFgR1drxTyO7//888+XW7bAIoOQ4RhwT10Pr6fuZpvoOjCtW7cWKxrXEa0mWJAozEa7gXhCkMUalwjWBHBm56Qj+ypb8S2ZDgqO9zms5YbvTO3atSVI+t133xUB7ldoU0GcD9WN77jjDreHo2QRFTNKWjJjKMbFBMwOj4J0mViwc9u9xupwjItn6dKlUrE0m/C+LAgIGRYninrRk8hrLjg3cdaBee+99yRokxYTuIro/fPBBx9IMT36NeUmBJPN0MktEyov60WmRGimRVO8z8GxI8OPasC8Hy6+smXLGj9DHBiB/riaVMyECxUzSloyY+g+XLBgQXPKKafIJJKJ3jq57V6jOxxT3Av3Q61atcSPnk1weVCgz8bM2EyQMLiWEnGX8DvcKlasKEKG5o90Xb7gggvEQoCQIcsKkUyAciZiTKKvJ4rH5Wa9yKQIzbRoihc/8/7770tvLIQ3hSStu8/vghtXEzcai7KRUMKBipmAkwlfvPM1mSSZANkJscs+7LDDMjIRRu9eeU/ex36u6A7HxxxzjFhpKPTlRjYGAgZrTFhdR1jnELVWyDk7aD/++ONyDmvUqCG9ghAyCGLSpqdPny4tDxAylKi35zfdQjD6ekLMxLNeZFqEZiMo2H4O4rkIksaFhyWG48/3hKrAXgpSTwUsTYhhakuFrQdbmFExE2Ay4YuP95oE2doYkWihke7d66mnnipp1qTaOoONnfET3PP7mJzdxIsZXpl2HWEho4UA8S5U3HVmcxEfg2ihnQEQ04Srg3grziU/52+ybQ3JrxUmr/Ob6PnPhmhiLAgZKjoTm1S1alURkkWKFJHvbFCC1BEyFExkg6ViJjyomAkwmfDFx3pN58LAPTui5cuXpz0DxFqBJk2aJNVJqSNix2SDP4mfwE1BMzzqy7hVI8MGIxOIaGNn/LzbTQSuA1xHZJDRQoBUaxYWgnntNYMFBsHCeUFE0LMJS0y2XXLxrCHJvC9WjhEjRog4i5XBl61qv4myefNmqR+DkMFySSYY937pL5UMuJmwOiGSacWgBB8VMwEmExNUrNd0LgxM4D179szIDo/34fbtt9+KkIkXbMxun+JpTGZuYBcx4nZwezE+v+92E4Fzc+ONN8qOmBR9FnvqvYDzmqFEvrNYHzc3XHKpWEOslQNhHS2qvdhNGysZDT4RjXXr1pXGkc7Ytrzq0viNxo0bmyOOOEKuReYDJfiomAkw8Xafqbg+8vLvZ3qHx+vZlgQ2LsM5hsWLF0vRLyYxtyZhu4ixA7ZjJsjSBr76cXFIFCxjWGKctV9iuXD8KOqc3xvuEWy5iWqvWDsQU4gXAmL5buCmtWMhiwysZTNeXRq/XbPEAFGriHngwQcf9E0VYyX/aNdsHxBdPyWViSUbcTQ333xz3DL56SA3MYavnFgazMtkV7mB83ggYmx/oLC4m9J5vXqFWNc4gcyxgp2j/y6d8T/Jvh4usIsuukisZG+88Yb0sLKvgxuQQoVw3XXXScwZr5muruluQ1sLiv8tWbJE2lgowV6/XZ1lKNB02WWXmZIlS4pynjFjRs7P2O3wheLLh9+d38FMupEquCHC1k/BZcKNCYjn8kss03eqRL8mF14mFzG7g4x+feJ0iNcg6M8tIWPHh2BhEaAsP4scQiadxzxVoZHKNZQXNnbJthLwOokck1jXOOd48ODB0oU7XiPGeNdqKoKKeZH7vM4hsWO0dCDD0DbjdH4enqOUATeCtu11aS1KCBk/x8/Uq1fPFCtWTKwzSvBxdaah4iR1ScaNG7ffz3bu3CmTRu/eveWeYE7Muuy8gw6TFOZfbkyytn4KNyagVBbDTExUXpn86MFUvnx52WUmenzZsWZicXcuYl45PnYxvPvuu83QoUPls7shdryE85gMGzZsn2PiPBaxzmE6hUq6NyLMlw0aNBABQz2Z0qVL7/Nzxk8PLJ7nxv+d6ehWjFNN1w+CNBYITFLuSUgIy/UcZjzjZsIyQ42JK664Iu7vYC4866yzpNrsCSecEEg3U3QlW4pawdSpU+UYUdk2VTdFpmvPuDH54VYi6JdU0yuvvDLuOFisWLSos8Glz3XEzjXT/aTcPj7AwsyiTT0gmjPyXUMA2hYU2YyV8MLxiD4mWIPbtWtnunfvLj9ztlu48847c1ocpHvMiR4L5/nheo0nNMaMGWO6du1qWrZsaZ588kmJHwmLOzAahFydOnXEC8C94i+SWb99FQDMB2JBJ0o9Hnv27JGb82D4iehKtkyypH+SapjspBNvksxEAKbbQZ2UvkeY7N69W8zwsRZjm4FCeiqTOPU1sM6QEsx1lclsE7ePD3AdsDDbqrssjGSGkRrNLjbbvYicQiGemyYbx4SYFxY9gnmx/lqLh223wM+4PnAnJXM88hIL9ueJljLIK/ie17v33nvNkCFDZKy4wPKqbZPfVhB+gQB0LE+4mlTMBBvfiBm7SGGpyE2hDRw40DzwwAPGr0RXsrXZIMnumvyQkZCu3TkLMpPVoEGDJBU43mJsM1DYqbJw0VWXcufEZPk5NiCv4+l8HuFAI0euDSxTWLRI1yU1OpPZN9GZQM6+TLiU6cHkhqDhOBG8y7Hg2iBQ1H5uhJYVOQTSJirunCLFWlidAbax0vftBi2v94gnitnA3XTTTfI9oHs9lhnl3+NFAU0sVHSvd0s0K5kn4TNL4C1BuG6A+ReTKRMOAbC5QY0TJmynZSbaX+xlcusEnAxu1rhItD9PqmLLvk///v2l+FrHjh3NE088EXcxdqZ1U9yNgHKgqzW9pfyM09rBZ6Pei911Rx9nXEsIQFt1F8tUOsvqR5//aEtMt27dcixE1D9hwUdQYk1wQ3CzwOFaiv7czCNsKBAyTpGTG06RgjiylY6xtrIRs99B+/10Bt1yTDCp58diTbdo3pMijWTPKf8frNrEiL3zzjuS3aSEXMxQRZFA3byCKzMlZIiT4WLMy2/Grjuej9gvpMP8m65ddrLWExvzw+SNhcm5G82v2Io1BrtoLFy4UNKeMa0fcsghuS7GzsWaRYP4GhpCsuCUKFHCtGrVSrKPvGbBSsbawXEno4tjQoVXRHGs4xyr6m463GGx+jOx2Ea7bBA0WGQQMsTsJGP5yASxPjfHBYGVzPXvrDGERYeAYV7HGWAb/f1s0qSJJENwDEj3TkbYr1+/Xlp2cE8qsrpS9ofjjOURq5WKmeCSsJhh90vAGUG6LALZ2MlaIcMukvL0bscc+Il07LITKdce/fqY11mgiPnBfM5iGkuYJSq24sVX2EWD7C4WQ7IW7OdOxEzPOPlbRDKLLQGxPGYXl+3rLD/utljWDsQD4o7vDeIFYWN7VEUfZ2mEicXyhx+MweJauHDaXUgIF6ypnLNolw0WGVxLWGSSsXxkm2TFnb2ugUJ1WEliZT3FqpqNqE7Givrll1+KEOK1SMOuUqVKPj9lsEE8870ePXq0WPb9vtlVUhQz7LD54hDtT4MygiipEZMK9G+hyZyFnSKBcAgldjRMBHy56Xy8d+/eHJMtP7cZGIo75doTcRPlVnUzUbEVvTjaQEz+BqvChg0bJF2f+JdksKmpVEVl4adHDWms+THzp0J+3W2xjgtxErwe9Zp4zOeL20Bx3jxjmjfHD2sM1s5p04xp0CDhMTvL3kcXk4sOqEUsxnLZMJZkLR9B2kQ4K+4ma0WlkSfZaGXLljWvv/66ayEAfgExQ1uDN99807U2J0pmSSoaisUDV8/YsWOlVDQ7geiAKr6QibJ06VIpbGSxsS6Yx/v162deffVVeUz5bSdYaS644IJkhq4kSV7l2uO5ibiRPo6VwwYvpyK2bCxBrEBMxC3igzgZFgSESbTbJLf3xqWEYEawYXEkXZs07Wy6mvIb2xTruGDt6NGjh7QTAKc1YJ/X3L37XyGzbdu/j7nn8aZNeVpooi1CfFeji8nFCqiNJ1y8kOWVbpL9TLEEUG6ZULhLOO7nn3++eeWVV3xRcsJt2IDXqFFDjp2KmWCSdGg3ZnkKMvGl46JIJTocQZJbmRuPlMAJJXn1QIrnJrIigZ1QOnbb/H30rh4Bs2LFCjNx4kSp9ourgngXquzSPoA4GK7LvFJjeczv8VoII643dry4NrOVsprf2KZYx8Ue7zzHThVtZ8kCvmc85vny5XP901huJI45OIvJxQqoDaJwSRfOYxOv1QDnGmspx7Z169Zy/auFOnGYkwh+R/TjVg6KNVD5PyJJ8Pjjj0eKFi0aadasWWTz5s0RP/D777+jiOReSY69e/dGtmzZIvf5+XkmxvLXX39Fxo0bF6levXrk4IMPjvzyyy+RVatWRSpWrBg59thj5Z7H/P7YsWMj55xzjtzGjBkTc5w8x8/OPvvsSLly5eR3+btsfKboz5af98zP3+7dsSOyt2jRyD8FCiBjIpECBSL/HH54ZMv69XFfh+P+9ddfRzZu3BgZNGiQHOdq1apF2rZtK3NBpq6DbF5jXhkH73P99ddHSpUqFTn++OMjV111VeSnn36KdO3aVeaynj17Rv75559AHNdsjnnNmjVy/G666aZIu3btZB7x0+cPI78nsX4nLEtpVkadF1xMWGaCXmwpWYJSBt75OfIq156tcu7OGA3cGFgESCum6B0uojJlyoh1wFo5cDXl1nsm+jOw68VVZTNOsPBks39SKscxkb91nlNxEz3xhBleu7b58/8CISNFi5pX27Qx9/TtG7PnD4HguA7JlKHXT+HChcWVVatWLbEIWfdiuq+DZHsRZYpsj8PGc5UqVUosL1y/ZCxR2ZdMQQpEptIF2ivHNdtjptUJ1sN58+Z5ok+akl4S9hERgEtBMr5giv8K1Pn1c8TqVsxEhHuDnxHnwsKKa8mZamwXBDKqIDo11gmfkdRNFmr7Pnbhd/vz54dYAbq2/gzB0vz/J2PM7FNOMacVL25KnXWWWbF2bdxsGo4rf0PNJlKIEZMsqrbvVKaOkTOeiGwdeg1xnrJ9TqLjmhCGmfzs1lXbsGFDc99990mcIscewY17KVXcrEHl9pgR5bSvIN4oCIUylf9Pwt9EahiokMleJ+ogfo78WK+ix/Tdd99J4DhF8pyBj5JqfNJJOTFcdkFA7HDLK6jXBmFSQdqmyjp3gM7mlF7eyUY3TmS8NsaFxxQKJIuQSfyvggXNqr//FiFDXFS8RpgIRJ7neHPc69atmzFrjBPGQSNaWi9QEI5CgKl2jc/vOGyTSSyAFKaLZyFIl4WW44pVBhFHPR7io1iI04FXGp+6MWZKFbAxp8efn5toKvujtZ3TQCbLwGeTTJezz4/VJ3pMTz/9tExGXbp0kcBdm36cjuKD/D43ygMgnhABZEjhxiLw0mbXsahEiyM3GifGek9ngC7N9RACiDxn/RksNH369DFz584VlxrHtUOHDuLCizV+BCLBqBwLyu5zTLP1GVnIEQd8LsbvRi2gWDVhoi0EVuxiPVm1alXKjUu5BnEt4Uol1b5mzZo5WU626GO885XM5/FTECy1pLilIqJJYUeMU+6jc+fOaR+j4h4qZtKAXyeHbH6ORMzE8eJ17JgKFiwoVgJcTTTUy8Q4rXhCEOBSwVLD++BipaorqdvRC2qsqrfp7gETr0UAVioabJLVxU7eNk60QoYCl7Vr1xZXHJWBbb8vBA4352vmJhD4PKRiZxPGhjCgsjPngoWIqtJuuADj1YRBVHCcJ0+eLLGEZHtaAZ1fVwhWcDq/Izpfe+01sYZFX2ccD9tTLD+iyQuZZcl2C3d+v1I593x3aX2yefNmU6xYsXy/juItVMykCS9MDl7+HHlZfWwbBCwAxG5gTnZaQBgTwoKdOrVU0jHOWJOpFU9YIWhc+cMPP8gCxWJuy9MjBqJr7kSnK5M+m67FNpZVi/dEyFAEDLfRkiVLpFYOgobJnomavyEFFVdF9erVZbHlc8StP5PgMXKjPABCingR6qpgGXErpiu6JQbHmCaGxBURKG2bPkbXZUoULI8IzwsvvFDcWYjn6A0B1yQ3RGymu71nimQsten+fiEUO3XqJC0/CP5XgoGKGSXj2AURi0o80zg/J+Mour0ANV9s/ACuHipQJ1vtN9nJlHu6aduFlIUC6wALqm3gGF1zJ7rqbToXmFhWLd6TxQwhw+JJ00ZajvTt21cEC//HDcfzLHiIAFwWuJdyWwRYkBFyxMfwe/RwwuXhhniIthRyz1hoertr1y7XAoKdLTG4ZhHfHDfOPRV5cYUkaz1ggUY8Y2FDzHBt2srb0RsCftd5TcarWp1MZqKXA3rT/f2i2jdikQJ6KmaCg4oZxRM7MCYsXAgspJjtafzI4xdeeEEWMFw8dLcm8DIbk2n0Djy3+ASei1X1NpNWLd6ThQ+LDIIFlwMpvPZzIP6w1JANQxwHC67tOI+gieUG2717t1T25jU5b7j0eB3iZOwxS6YZaDpwWuB4baqBI9IoFHjTTTflFJRzY5G216zNmCNTjDElG1NEDBjxG5wfKp9zfmKlXjuvyaJFi5qRI0fGbE5p43dwfRHnFcvS6af4vEx8v3A1UUWZBp2a2BIQIgEnDEXzvFwAi3FRoOriiy+Wex7Hg/FTgO3nn3+We278TaNGjSKFCxeOXHnllWn7jLwORbMooNW3b9/Irl27Uj6GyZ4H+3m55fU38V57z549kT59+sjnePTRR/f7OcfytNNOixQpUkSOYZkyZaToXfTvURSPc3TggQdGChQoEClYsGCkRIkSkZo1a0oBt1ivHX0sM1mIzH5+Pg9F5Cgox42x5XZNZZpkzmEsduzYEWnatKkc74kTJ+bre0XhQgpFcg45PgMGDIiceeaZkaOPPlqKnB5yyCGRkiVLun6s0vF9Sdc8x3pA0c2hQ4dm/L2U7KzfapnxOV6sDZPKDsyZfYTpnkJXc+bMEasBVhviatKxE+fvMecTYIllg+BY0oDZ9eX3GOYWxxMrgNeWrCdLh5RpghLzKlAYDZYTXEvxrCIcTz4nxQXpPo91i1gB3GU2sBSI+SDYlr/HUsDOH5cJu3nSWXNzUcSzcqXLWuO8xrHMkI1Cg1FwKyA4vxlzTrZs2SLNegkux4JC9lIqgeq4HDmPlC/ATWuvD+JuCJ6OjvVym2Tj3tIZz4cbla7muJroGeanOVWJjYoZn+P1Alj5zZCyEwqxHpiC8ZVjOidG4dprr01L8C8xCvjhEU24tnDVEH+SrlgMZxotrgBnNgZuK2qnrFy5Uhaep556Skzf+VkYc5vkbYVjPhMBtHSpZ7GjkjdBkNYlYmvJAJ+dLCgq/SYSZ+EUrLbgIJ8JNwhugfxk3DiFkPMaB+KB+DyILkQomW2ppkJnG1xTVFXnOiBtHhGdDLEC1REyBMgjjnEjshEg2BV3SnTgt/Kvq4mYPL4TFSpU8M2cqsRGxYzPSSRLyO2U8fzsqGxAMJM+vnKEBn7/3Cr5Jvv6LAC8LjtaFnMbdJyOWAxnOmnp0qUlsJlYFJuNQRE0uviShgvpTuV2wmcgUJQO4RMmTJCdPGIFqxSfFYsQ7x9dRTnZhZVgU2KcEBfEONFywjZCTCbexrkzpkgdcTwILI4b1zrvwY4a6wyBwKmmQmcbstCwCiByud4QHfkhOlD9qKOOkmua48IxQciQ9ZPJa8vPcA6wWnEt3X///YGrGxY29Cr3OblZPvxsLuWzUCgMlwgLFOnY0ZlEqQg1/sbuhimK1759e9OtWzdZCGwvp0QsQPHGEF28jvfA6nPwwQeLtYIduTVv42bABZDJhZixsbDhriP9l10n45gyZYrsTnE52SrK+X19W3DQprNTFwgrQXSacqx2C852FbZ9AoGrCCICmBnbFVdcIQs0x43Xx2WGxY7jSk0WP7SgeOONNyTbiVR5CreRWZMK0UHBdIXGdcl5xq3IdeYHgecGZIM1bdp0PzETlLphYUPFTACIZ/lIt7k0m1YeXp/JmYWKWir0UnGKGGsFyG/acPSEBRdccEFOLEYiFiBbGwfhQ+yG05Jj00mJx8Hyw+LLgotosNkY/C4iLVvHFEHApI2Fg3gdxoVwGzp0qLhurBUlv8RKHa5Spco+acrRAhtxgrsNSw5/N2LECHG9WfccrkWuX7J7vvjiC3kdm0GEMOR4siDxXm7Wn0kE4pYooY9FYOrUqTLmdKeKc/2Sgh9LRCqxXU2Ivy+//FIEZtDqhoUJFTMeJR3CIVUXVG476EwvGMRDEAvAQoXIcI6JcbAAsqCRNswiSOwAJvdkBY1zwiJtFSsFJBJfwOLx3HPPiSWCFFniOHDnWCsFiziCgYULocDCy4LrHGe2J00ETe/eveWY4VZCLHA8SXe2RfcSwQpK57FKJJ3dKbCxvmzbtk3OHXFLCFaEzOLFi8Xlx0JDDx2Kx9nKxbwXopGgZKcVxrYYsC0okr0WMgnH+oEHHpAbx4fu15lw/TiLDKajUm4YaNSokRw3vqMIesW/qJjxIOlyD6XignLGfLBAE0SYzaA4rC6YyZlknNjF0DbB5HMRA4LwSTUIND+ZKSxUCBZcNmQkcZyshcYKB/zyiB0sMl5YZBkXtUxwRWChQThgQRowYECeRfXAmYkFzvgipziLdX04BTbXFeIFQcNYOJY8R8ViRBVWBuqv2AaLToHpPE+2xYDN7EnHtZAuEGmMA6sMx5d4olg1ZNJBrO97Jq2pXojHSxWuM6yDuJpw0WXq3CiZR8WMB0mneyi/LqhYJcRJi4VMB8UxSbJLatKkSc5OM9ZiiLkea8jgwYMlviLbmQe8D3EcvDfxG9xHx9ogHIjx8Nqkz7iYvIlJQcgguLAwJXL8+B0CV3FRQTIZZtHWG6o6I17InCKrhAWfBcUWR0vEQhad2ePGtRALRC4WJEoLkK1GNlmmcX7fMxkz5+d4vGiwAE6cOFECs4nTU/yJihkPks5o+ni7p7zeg8fskm0JcWqQsFCwaGV6UaaHEG4PJphEYl0YfzYyD6KPJTcbxDtz5sy4sTZe9b/bKsEICIQME3kix4/f4XPayreJZpg5j589HmR1IV6s5QqLC4HKyYo/Z2aPF7JQSPW/9NJLRZS9/vrrUj4/22QyxThI6cu4sXEBYwVWMeNfClA5zwSYP/74I8d/T6Ekv5AOE24irqTcerdgIicg0y40BC9mevfF5Ug6Llkec+fO9Yy5O7djGSt+xE8m+GTG6qydY119iVhP8jp+6TpWXjjuCH+siqSNE1PlDF7PJs5jjrhL5/c3k6/tBl26dJGaVlgb/fw5wrx+q2XGo6RjN5/I7ongSmIfEBG4TLA02OBEN1wkpKt+9tlnZsGCBQn/TTYsH7kdS2esje2Lw3FdtmyZL0zwsY4fQja65kwqroW8jl+6zl+818qWyMHlhkWmWLFiUkqAIGusjG53+M5Ez6wgpS/jaiIwG0s04lPxH/6+ApVcsa4kis3FMrvb2AebjUOV2oceekhM5Ez+EMtikykQVLx/7dq1PTeh5HUsnYs9WSQIROfC7ScQMq1atZJFmXsexxMk+T1+7LasVTDT2PNCk1LuM/WetCSoX7++pKPzf9Lx3b4GMvn9zebckGlI96cLfXTCgeIf1DITYKKDLaNdITb2Ab8+Ey+l0MlSoTIshb2cVpp0kdsOmUBJquS+9dZbnssqSGQnahd7aqY4U2X9UOvDeV6wyGBV4jH3PKaYXqw4q0QtHtHXIsI5W8GjmYjviP7cfG9oD9GsWTOxxhCPpFVk/QPnEOsMncZHjx4tcYKKv9CYmRBgi7vhvwdSXampYmM7cIvwcyZhp+Dh9yh176y6m+o44rkpuAzr1Kkjxb4w1XtNzCRCdBl+atZ4ceeaW/sAzgvVkEkzt24yKgY7XU2J1B6ybioqH5N67RQ7XGNYSRAXWGoeeeSRjLoJ0x3f4SxbgMjjc7AAEndBdhaVj+3vBcUNEwbs9U6RzsaNG7s9HMVozIwSp8+RTaXFtcQuxC60RPJTD4WKpC+99JJYaajXwYRN+XVn7ZRM7ZCJkSFFl8aBVsj4bTHwQxxBLEEZfV4QH/H6NDljUhAlsc4ndWtISaaqKhNQdDfybPe+iZUBx9jjFfbLC1u2gO8R7kSqVBNv5hQy9n39muETRig9QZVuXE0qZvyHipkQwGRtS8DbSqrRCwgLlu1/NG7cOJmkcZfEqp2SyjjiLWLEymDNoHaMn+tYeH0BiyUoY50XPkdefZriuZ2oEfTOO+9IFWfir2hLwXXnrMybbdFnz4vTqoJgp9FosgX2GHPZsmVl0UPI8JjXodaQl8+9kjtco8xzZHByjVD7SPEPKmZCAJO0LQEP8Vwf1kpDFVgm59xqp+R3HLEWMSwy8+fPl9RIa5UJUh2LeOnb2XjP6GMdT7jkR1zE+js+IxZArHyIBSw73GJV5nXjfNrriqB3bgR9cs3Z6ysRayACn2sV1yjBvrTUSNd3RHEX5kjaTuBqwtqm+AcVMyEhmVL91krTtm3btC++sRYxKtFWq1Ztn8kj266IdBBvIcyt/H8mxxLLshVPuORXXET/nbMbOUKB8v2IG69U5o1uhmktM9aqxDGjEmzp0qXNfffdt1+vKtovXHTRRfK7VMcm5ge8GBulJA/iFLcoVjcVM/5CxYyStj5F+YHsJXZBBCc7FwM/xJ84yc0tFl3+f+HChbIDTMfxjSegslXXJZpY5y1eZV43YqKis6qcMTNYlRAyxIlhSeL/zuab1CC5/PLLTalSpaSqL/fpwm/xYUEGVxPWGdpR0OZD8Qf6rVFcBasMQXe2W7Vf61jkVoPFpsCz2yflc/369dJIM9V6J7nVT0mkLo7zdRKt+ZLI70afNysgyFoikwiIpRkyZIi5++67M1r7JbfxYYF0jpNjxDlCyBALQ88qrDOMlcD4hg0bSpAoz6dbyGSjDo6SGGw0qN5MMoLiH9Qyo7gGlX4pLvbEE0/skwXiR3Jzi7FYkgrPYvjggw+K0Fm+fHnS7pbo3Xte1pdELFvJBFqnEpTtDMClDMCTTz4pWXPWOuWFmCjGiHjBIoNgIZaGaxOXw8aNG2WR4/HBBx+c1vcNWnyY3yG4m4QJzjtWGsUfeH/LqwTaKkNwKDEkfifa+hC9yNvsoPPOO8+ULFkybhXceJaPWLv3vKwviVi2kqnqm0oFYOdrkB3Ha1BTCAsIDU2pS0O9I7etErb5JufyqKOOkgylDRs2SHuCkSNHpl3IANcBx6BEiRK+iQ8LOogY3N9+q94dZtQyo7gCMSRkhIwfPz4w1TbzikXJqwou2LRhFjc6StsaL/F276nGFUVblKzAivV66QjKdpYJgKZNm4qAoHJudEFHt2A8ffv2lbgYRFaRIkVM+fLlMzImxBvXAf2bsFLddNNNvnCrBh0qoHfr1k2ErU2EULyNihklo5A1ws6W6prOGy4G0neJm2DxvuCCC+TG7jTIWMETq+AccCzIkiHYFDcHLSVyKzSXajBvMm0GYrmukg1cjS4TAPSyilXQ0S2wxiCuVq9eLeKKa5jaTHmNKT/p9xw73Fq08CDglEKDkyZNkhIJKmrcA2sn8xGuJhUz/kDFjJI2mMy//fbb/YTLli1b5OeY7SmMx66H9Ed2vSzcVP/FQmNTI5lE6tWrZ+rWrSvm/SAST5xgkUHIYK2i+ae1wGS6A3I8gRW9gDvFU35jaJyZcrxGXgUdMw0BvxT049rlRvVjBPi8efMkcDuRY+5sGRKrA328v+FG0DFCBpcbxQapPtuhQwdpJaKCxl1XE9c0AeCIS8XbuNqb6d13383ZmTOBYtJz5vYzNMy9EyZMkN0S8QbU68irMqkT7c2UGShZ/9VXX+0jWghqZWEAJmiEi/PGc/F6LnH+ETXcKKDHogLUn0HYIHAQN8ccc4wJCrGsGgg8KpAiZKjXkmofoUz2MEpXj6V4Fo3crD7JWkFoooqlhZsVLfb/BPdamCMQIHRtnzZtWsI9whhL165d5frlO8CYbr75ZtO9e/eYY3Me6xo1akh8BkHHCBqELBl+CCPErZ8ETZBSzDmnWIqJlUJYKtknmfXbVTFDPQeqvzJxNm/efD8xw+Q4cOBAyXwgUJT+QV988YX4lxMtNa1iJnXo1UPmkVO4IGQI4GSyZ8J1ihbSV1MVHeyMrbDhnp0znHzyySJuuLHgYO0JGm4uCMm8d7obOMZ77WirT7zGqbt3786xsESLFq4nC/MAG6IKFSrIvfP/XLc01qQfGe9vU8kTGa/N0qKyMJanWrVqmcGDB+dkcTnFF8fYKQRp50FvJ7KlsNKQ3Ve5cuWY3eu9Khj82oIkN2ivwtqBtVTJPr4RM05YFJ1ihmGR9cEXmd0N8IEw9/GFx6+eCCpmkmPz5s37uYlYDDgfBEYiJpzChV0lVVQzDTEVTnFDE0SuGdxV1i2FuKG0fFjwwqKWqTHEs/pgYcGSe//994vAxpJFYTPem9+10A8qWqjY/yM08rK4sBPHIoy1BFGSCLYDPfPT559/LpZkG9gdXQGa5218khWC/D2p+0899ZRYogFxhcuJnmk2diq3uCY3z3e2u6FnA1yOrVu3lhg/Klor2SUQXbNZrCh/Tm0OCx+KicUGCcYCMy0358FQ9gdx8t133+0nXKzJncUAC0uTJk1yhEvVqlVdyzzCRcWkwg0Yu3VL4Q7AFMwCxTitW6pOnTryOdxe8DMBizg7+W+++SbpRonpJBPVhClYhiWF10YcMJFdddVVIqopOGjBeoGF9vjjj5cWA7hmrHBJRLDkBq4+BAPvy30i1ZptLBDinve2aebRFaBt49boGChuti/ayy+/LAsoG4gZM2bIY757ZH+tWrXKldYQeVle/NiCJC+o+Mw19uKLL+ZsqhVv4lkxw5cVogOveGx/FgvcUpSiVvZd+IjBiI5vsbs/jikiANO6FS6ZSkVNZ2ErdqvcEGa4F0ilXbx4sXn++efNsGHDZPyIIBQ9wmbAgAEiiP3cPNK+L4stWS9WXGZjUUunFQbBwjmLFcPiFCws7IgUBArBwdbCwvWJmEE0ZOLYIyKo+suizMaJbKPcgnktzoKI9t5WgLbp6LYpZSwh6OxeP2XKFHGrc0zsnIfFh00GnzvbgiGR4n64xbj5pXJ3XjB34Goiq0nFjLfxrJjJLz179pT6HE7LDAtaWGCRYMJzChcmROIJgEUAscIX0woX2yzPryBm5syZI5+bBY+4BSx7lCMfN26c7GQ5BmRMEVRrLTe4AfLbe8XZPDLR7JV0wSKCOEXIELdEbEWmF7X8xENwLSJQWNRZjK1Y4d5aKYBzYF1ALPRO1xDZbKlYWFKBlgW0ncA6jFuLZpl5Ea8rOTE9tmWHXejjiUPbvZ5WD4hlxmDjg7i+cUk5e0pli9wsL7Guj6CAFQ0LHfMIwlrxJp4VM7beCGlxzsWWx+xM4kGFzkxU6fQidvdnRQsTCYscEwu7VtxCiBV89DYwN52WCa8QvWPE4sRiSHYJ1wIxFghaFsa3335bdrwsTAiPmjVr5gQUs1BQ+yYvOL5YFRYtWiTBnmSvEF/BwmLrwmQSFhEWE0DIUKcl0+/pPMYcTz4/DSTJasvNwmJD8rB0cD0iUEjFdsawsHC7JVjyAtHLtYK15KyzzpJEhdzIrSu501WViDi0f+Osy2OFEK+dbatgbuUBgtyS4eKLLxbBjagkCUXxJp4VM2QvIWio9WDFC1YW6pKwywkTLAjEskTHtxA3AlQoJRCW1GWqViJcqlevnnDGl9/JraBcdEE4JlgmJIKFSYfFLcXzuKCwdBCTZQOKsRJwbJ3YRYgFHfcd1yiCBtHkrAuTSbLdURyrHosUApkYFuJ0yETkeYSjFSy4hKxAadWqlWxCcM/we2XKlMnJ7PEbCFTmHVw/CDIEZKpxRMks/rGEUHRAMYInW4Im1jiDGC9jYQ7A8oqrCQudV4V30mCtJ0ayZEljArBWuCpmSEFkF2fBNYClgXRbIsdZmOnfwwRpU7PJcHKmbwcNJiqOSbRwYREBJgnEypVXXpnjJiI12u+NGjO1uMcqCGezThAhTExYGAgixYS8d+9eWSi47rAmYEWwbin+jxWG18FtgpAhjZdmmZigcWFlaxJPd+AtgoOYjlgWFlxCVrBguUIkc2ywdvXr10+uQQQMx8M50VsLobU+2CJxfoul4DNNnjw5xzKDAM6vezIdi3+8gGI3hWK2BXa24fiS2YS7mgxO3zNvnjFYGUmQIUto2jRjGjQwfsbV1GwyUVgooiEQlfRGWzSPnTM7wNq1a0sth2T8ll5OzY5VeI50U0Se9dlHF55D5AVmZ5AFbFyCtcxgUdm6dascdxZazgGxHfwfy9aoUaNkMqasvE0FX7hwobwG1henK6pRo0amU6dO8n8/TOJk+SFYosUK9wg7p2CJV4cFVx2xR4nWlrFB0pjo2aj4uf7IypUrRdDgdiDIPNXvYX4DqqMtM17oZxV0mCewNHK9Y8X1vUWmeHEKiGH2R62TvkoMh+csNL6sM5MpvCJm8io8h0CLFi5BqnbrBtFxCVRkxfpHxhu7ayYo3AbEYbEQsCjYwMXo/kMEF9saN4gbrifMz4gb65Yi/gaLRV5jyqTwsYIlloUlWrDEEivcM2nntlAn+xmc9Uew3hDYivXVjxkvpEiTrUNaPLFKbuFWJl2YYf6YO3euxIj5ekO5dq0xJ564//MUJi1f3ngJFTMui5m8Cs8RzxJdeC5Vs7WyP7GKeLEA25gXYh8IEubaAGumzyswE1cUwpQ+OgQUY+bnOkMgkCFl3VK4nZx1edJVIRXBgiiLZ2Gx9U2sYIklWvISLMmQl7hxxhlhdbTuEb9aFAgGJjWeeD6seUo4YDNTv359+b4nWkjRk+wOpmXGswHAfgBxQmGraOFiS6fHKjxHI8W8du9KeoiXJhvt23cGVybSbBGXFOeVSY24kUsvvVTcD/QaY8LDDN2rVy8RqIgbJj6uAeK+Eg36xGqUm4XFChasQ1agkPHitLAQX5bpHWSiWTk8z46WGBsvdcjOD1j26HRNqjWfm3grv+KFKtJ+gaQBLIu4GH0tZgoX/jdGxsbMIGR47DEhkywqZhKEzBWyOKILzzEROAvP3XDDDb4pPBd0ckuTjbeAJhqY6cxGsRklTHDs2rlWsEJguSFAmB08ZeoRt8Q8kfVDWwhELYIIS54zvZl7RHK0YOHGAuq0sGRDsKQjK4djTqA1GWK2FYUbHbLTASn9LGhcH5wPBKwfNyhB7KWUSdjEcL6J/xo6dKi/ky4aNPjXEhOgbCZ1M8WAgFCi1p3ChZgJW3iOHbbTTcRE4PfCc17BCzvFRMaQSJNF6+bCUscCSPo84oX6NFxj9JGyVZjJEIoXw4Jg8eoik2yzyehYD3D7fOcXLEu4mRABo0ePNn4jiL2UMg3fXwQ5AhZXspJZNGYmiYMRXXiOG/VCiItAeeMWiu4IHaZmhtnEbzvFRGNFohd6XEgU2SMTisBvPiuLYSL9f7xIKlk5XjjfqQhositpSokFjto6fiKTXc+DCsslm1l6gXHslMyiYibGwWAHTPBhboXnCMR1ChcCdaOLpimZI4g7xXgLJWIZiwvCmHpKYVxIvHC+UxVUTJ8U06OPE7t2Zw0SL1gZ88IPY/Qa9957r5k4caJct2413g0Lf2gA8P7gr7fm7ViF59ghZ6OvjhKfIFYRjRefg9Wvbdu2UrOF+zAuJG6c7+jFO9Uy/MT+UPOF7DYK6hEYjEDNpNUpnQIkE13Pgw4F9BDepGkT2K94g9BYZtgBUsFVC895mzDtFO0iTno3zQzDSDbPd7xmiOlwtRDATSo+hT1nzpwpnykTVievuObCXD6fJZP6VGQw0tRW8YZlxiffgtQhVZY2CPSIUSHjXexO0TcTdAogrMuWLSuF2MJKNs93tBUGSy3PUQwNsZGKqw/LLxV5Z8+eLan51uqEkEmn1SmWJck35fOpbUKxNu557FNYPygpMH36dAnkV7xB8FcMRfHwpHjVVVfJpEgMjZJZnAKDe1JssZ7Q5iIdliHaHNB+pU+fPmJtw2pCTRrOcSY+g29csVhkqGlCkTbgnsf/lx3qRxAzVHWn4ariDULjZnK7nYGi5JbqSZsEL1WTDaq7z34u7nv27JkRNxBFFOmyvWTJEunMnk6XULbbGKTlOvBR+fxk4JxSOuHFF190eyiBRd1MiuIT8LvTUPSVV14xXsHGZWC14N4W73N7TCziqY7FurW4ZcLCwevTXZkJuFmzZtIDLF0uIXteEGFkT/nmOiBGhoXIuve55zHP+xisM7NmzRILjeI+KmYUxUVY/MiCQcx4QTR4MS6DisrDhg2TBpXpEle2OnSqsTKxoIkm53PVqlVSNZwS+OkQTNk+L873o6I1Qc75Ova2fD5l8yEg5fNpH0Ih1VdffdXtoSgqZhTFfYip2Lhxo7gmvICX4jJYPOlQTV0Pjg+LaroW8VSCj/OyFBHcjfDCjUi9qnQIpmyfF/t+iLEdO3aYQYMG5V9M2vL5uJa457HPIZmElhy0tlDcRwurKIrLMCHS24usJuJnvNrTyg0YA9YNipP99ddfplKlSrKYcnOz3UUi6dFt2rQRMUORNVK2a9as6avz4mwQipD5+eef81WLJwcsMT6OkYnnarrrrrvM1q1bxSKnuIdaZhTFZSigZ11NXonH90qKPIs2VgiaeLZr1056WBEz4mYsTzLunpEjR+YU6Pzll198d15sg1DOgRcsdV6jRYsWkok4DbeZ4iqazaQoHoDO2hTOIwOG4mthJVb2TF4ZSG5k+CRTaG/9+vViwaHVwVtvveXLbstBzW5LB7bgJRWBlfSi2UxK6ElX9ku2IC2bhdhLWU3ZPlc04IwV6JtbBhLBwYMHDzZNmzaVG60FMn3Okw0eJluNuAo6Lffu3dv4Ea9Y6rzqauLc4oZT3EMtM0rg8GvJ9/bt25uFCxdKFkxYqlQjRkaMGGFWrlwpO/+vvvrKHHTQQeJWQqREx2Y4LQSA+KG/FWLosMMOE1E4atQoT/Yb4vOQ5kyRRKqRK8GAeBmCpLkWO3fu7PZwAoVaZpRQ47XU4mSymlavXm2++OILEwacmUoffvih+fLLL0V0EuhbuXLlmLEZTgsB5/Xrr782Bx98sAQIY7EhmDqTMR2MmdgXbslagHr06CGxUQQGI1iVYEDgb+PGjTWryWU0m0kJHH7tvl2/fn3ZhZDVRHxF0HFmKuFiIoWZc1WlShVzxx135GlNs8HBQNf7G2+80Rx77LEZs8IhXnBj0YMJLr/8ctO9e3dz4IGJTaNY26ZMmSKFEhE1ZDphTVKC4Wpq1aqV+f777yVlW8k+6mZSAolfAxbZtS9dulTcLWFxB1I7BktM165dpZpqMucsm+cZVxZjxBW4fft22ZG3bt3a3H///QkLGlixYoUIGtoeTJ06NTQuxSDD9VCsWDHTr18/iflS0oO6mZTQk46ARTeCiEnhZbEjhiTo2EBaYkmo1UGsTLLnLJuBqQgm6gARH4F42blzp3nuueck5ieZa6Rq1apioaHRJfE9QQ1qDxNY2BCn6mpyDxUzipKL1YBdFoF9BKpmg0aNGsnEGJasJj9lyTDG2267zcyePVvuixYtKgKMuJ1k47KoT4KAw0313nvv+bJflrK/q2nZsmXiOlWyj/dnEEVxARYn3B+U0CdAlUDVbCwgFIVjh0fcjOJNQUO15j59+pgOHTpI1hV1gfITl0VVXSoDt2zZUoLVgxjUHiYuvvhiEbhqnXEHFTOKEgMWJ+I4yKwhQJXdVrYWELKaPvvsM8lsUrwJbiYsK7jI8tt3idfA1cTfYqnhWvNDvywl/kaETumImYCHonoSFTOKEgMWGDJqKKGfyu47P1x00UWmSJEioXE1hdlFZntyLV68WFxObnT59ip+jBHC1YTbkc2Ikl00m0lRPJgVhXXmhx9+kEVOCT7jxo0znTp1krTv6667zoQdvxa+xLqG9YwCmLgRldTQbCZF8XmAKllN9GmiboUSfG6//XapU0IcTliKJgYxRgiXNBsRdTVlHxUziuJBLrnkEqlsq914wwG1ZmjLUKFCBSmox040zPg5RghXE5sQiiIq2UPdTIriUagwS8zABx984PZQlCyxZs0aWbzpMUUPJz+4VjKFXwtf7t2715xwwglioUmmjpCyP+pmUpQAwGS4aNEis2HDBreHomSJE0880TzzzDPm1VdfDX3MhZ9qEDkpWLCgpNu/+OKLImyU7OCvq0RRQsRll10m6bvs0JXwQJ2h3r17S5uEOXPmuD0cJZ+upp9//llaXyjZQcWMongUzOsNGzbUFO0Q0rdvX+nETGaTBoH7D3pvlStXTgvoZREVM4riYchqevfdd83mzZvdHoqSZVcFadpUlOUa2L17t9tDUpIM6MY6w0aEjvBKyMUM/kbMrShciojhT37ooYc05U0JDVdccYVMjDNmzHB7KEqWoSs3i+GXX35pOnfu7PZwlCRBzGzdutW8/fbbbg8lFHhazFDt8rHHHjNjx46VLsI8pnz4mDFj3B6aomSFY445RjJbQterCUvE2rX/3ocY0pOZA+kPxk3xDyeffLJ0SFdXU3bwtJghk6Np06ZSc6Ns2bKS3UFX4dyqou7Zs0fSuZw3RfEzXPfvvPOOpGmHgnnzqPNPas+/9zwOMTfddJO0MKBC8NKlS90ejpKkqwmr6q5du9weTuDxtJg599xzzbx588yqVavkMf0u3n//fdOkSZO4fzNw4EDJS7e30qVLZ3HEipJ+aF5HzQ3SdQMPlpjmzY3Ztu3fx9zzOOQWGuqV1KhRQ+JntmzZ4vZwlARBzGzfvt289tprbg8l8Hi6aB4TeK9evcS1REAcMTT9+/c3PXv2zNUyw82CZQZBo0XzFD9z/vnny/U7e/ZsE2hwLWGRiWbNGmPKlzdh5scffxS302mnnWbeeOMNmRMV70OTWjwLoXMVp4HAFM2j6BAR/c8995z053jyySfN0KFD5T4elIDnQztviuJ32JFTcyTwZe5LljSG72yBAv8+5p7HPB9y2JQRf4G1mtRtxT/WGSwzGvKQWTwtZnr06GHuvfdeuRgIpmrdurW54447xJWULJs2bcrIGBUlG9Cvh468gbfMFC5sDP2oihb99zH3POZ5xTRo0MAMGDBALNQzZ850ezhKAlANmNR6PV8hFjM7d+7cr5Q1plXcT8lSpUoV06JFC9nV5OfvFcXtXXmtWrXCYapu0IDdx7+uJe55rORw9913SxzVDTfcYL799lu3h6PkAX2aateurVlNYRYzlHNnB4KJ7rvvvpOy7sOHD5cvcrKwm1mxYoVUVK1UqZK4qzSQTvGbq+nNN9+UgMLAgyWGGBm1yMTMknniiSdMiRIlxGK3Y8cOt4ek5AHeBdzEoclIdAFPixnqyZCWevvtt4tlpXv37pKiSOG8ZLn11lul+BTVVCk1fd9995njjz/etGrVyrz33ntaiE/xPLYS7Ouvv+72UBSXIRZw2rRpZt26debmm2/W+cvjsI7hEdDWJCHNZspkNDRWGXY348ePN6tXr5biRgge4nKOOOIIV8esKPEgm+Wkk04yL7zwgttDUTwA1wG7flK3u3Tp4vZwlFygRtrff/8tNaOUkGUzZbqyKpaeb775RspNI2buvPNOU7JkSdO2bVspzBdwnaf4dIeH21WLcClw9dVXS1LEXXfdJTW4FO+C6FywYIH56aef3B5KIAmtmLEQYEwczUsvvWR++OEHcT+hnAm2POOMM8zjjz9uttkCXoriAVcTMRJvvfWW20NRPAJtXigwSoKDLpTehVjPAw88UEqOKOkn9GLGyXHHHSdiZs2aNbL7LVWqlLntttvEWsM9FYgVjxKSXj4Er1evXj0cWU1KQhQqVEjcTQQGY6khhV/xHkceeaRUr9espsygYiYGpH9ffPHFUj6eADvMuNQIOPXUU80555wjsTakjSseIWS9fLDOzJo1a59K10q4IbMJ6/KHH34oqduKd11NH330kawrSnpRMZNAjYAHH3zQfP/995I9QBASjd/IhOrWrZt0884LothJydP6NhkghL18iJshMG7u3LluD0XxEOedd56Urhg5cqTu/j1cbqRIkSIawJ8BVMwkYcrF50msAtlPpIjTZoHA4bp165qpU6fG3CkjYP773/+ae+65R+5V0KSZjRsJeTfGBmtzz2OeDyjVqlUzFStW1DRPZT/orH399debdu3ama+++srt4ShRHHbYYebyyy9XsZkBVMzkgxNPPNEMGjRIGr8hYvBVX3fddRJjg2gh5sby66+/Sl8pAvO457GSRkLYy4frDevMjBkzND5C2cfyy7VBuQnmKDZfge/llSXSaV3H1UT8ZSJWfSVxVMykAE0tbbod1YUpwEf2U4UKFaSmAG4plDi1QQguJjuKIDAljYS0lw9xMwhjrj0lvMSy/B566KEy92zevNm0adNGrcEpkm7r+kUXXSThCmqdSS8qZtIEFYpHjBhhNm7cKF29KTnPglOuXDl5rnPnzuKaiu41paSBEPbyOe200+TaUldTuIln+WVD9fTTT0viwuDBg90epq9Jt3W9cOHC0oYCMaO1zNKHrqwZiK0h44m2CcuXLzdXXHGFGT16tFhnmjZtKinfe/fudXuYwSNkvXxwJyCW6Vem11N4wdIbz/JLsOn9999vevXqJb3oFi1aJHE069evN7/99pv55Zdf1GqT4jHOr2sKi/6qVatkjVDSQ2jbGWQCSlXjakK9E6RJxPpBBx0kVhpiax577DGzbNkyyZDq0KGDBOnxBVGU/PDxxx+bs88+W1xNBKEr4YTFEmsBi6zT8kt1cxrsPvvss+ISj1VOgsyaY489VuZIe2OedD7O7Wc8ppRFWI9xbr+PS4q1ACFEqxzn3xHrRv0yMmPVcpae9VvFTBr59ttvzSWXXCLmSC5c6tP06dMn5yLmUC9dulQC9MiE4oLGWoP7qUGDBuqCUpKCCbNMmTJi/aMpq6LA559/LiKGSrNslqg7Q92sP//8U+ZBEheYg7DMIGbq1KmT8zNuzJn2//bGRi0exAUmK4Kif8amL0hgkSHGhrWAc0CV5qOPPnqf36GBMpZ6as7o3J/6+n1grj9VkoIYBiwymzZtMocccoi0R0DN24sY10DNmjXlhtn3mWeeEfVOsDDZB4iaG2+8UXZKipIXTIC4miiWRqNBnRDDzZIlS8zDDz8sxT7Lli0rlmDmE6wysdwfWA1wm+QVy8cmjF5g8YROLBHE669du3afn9HxPbc4kvyIIOdjXoM51kuuKXuMY7mmcDVxjiiiRzsKJTXUMpNm2OGwK0LIIFoSmSg++OAD2SmxKPGYBQqzJDsmr3w5FW/y3nvvmfPPP1+uIZ0Qw3sN9O/fX2pgUX+IGBlKRRC/ly63STqgDlcsMZSoQOJGX7J48HnzI4KcNzLB0jXn5nWM+TkhB6TQq2U1NupmclHMpDJRbNmyRTKhEDa4rMiQQtS0bt1aU7qVmBD8S30jFq9hw4a5PRwlSzBtz5s3zzz00EOSbHDyySdLXznqDwU5hgV3lxU4yYgg543GwfGWPebrVNxl9j7ReZ9u51joN2zYIE0olX1RMeOymEkVTsn8+fPFBUW2Chc5JkmEzVlnnaXWGmU/3/vrr78uvne9NoINc8Ps2bPFEkMA+JlnnikZS2QuqZsx8c0mSRn5EULOn+WWRVi0aNGERBDuuH79+kl8E13PlX1RMeNzMePk559/NpMnT5ZifPSHotklritKlvOFUZR33nlHAsiJmWBxU4IHCyeF8BAxVI+tXbu2iBji7VTAZh+WTbLD8ususzfCEo444gj5/wUXXCAWVsIM1BKfj/U7EnB+//13xJrc+5m///478vrrr0cuv/zyyAEHHBA57LDDIrfcckvk008/dXtoisv89ddfkaOPPjpyzz33uD0UJQPn9qmnnopUrlxZ5rGGDRtGFixY4PawlDSxa9euyPfffx+ZPHmynFvm9kKFCsncrkSSWr/VLukT8IM3adJEKnp+99134mudNWuWRMzXqlXLTJkyJWYdCSX44IYkiJBqwAE3tIYGgmUnTJhgKlWqZG644Qap6Pvhhx+at99+W2sKBQgysAgCpt4M55aChkOGDJGsWKB6POf/zTffzDU9XlHLjO93bTNmzIhcdNFFkQIFCkT+85//RLp06RL56quv3B6akmXeeOMNuc6XL1/u9lCUFNi5c2dk9OjRkVKlSsl3ukWLFpFly5a5PSzFJT788MNIxYoV5bt97LHHRjp16hT5+OOPI2Hhd7XMhGdHTtG9N954w6xevdrcdtttUmkYVU+6LoX52OEpwad+/frie9deTf6EDBt25NSqothmvXr1pPUAgaHEySnhhArfX3/9tfnkk0/EQkPcFD0AAUs814jyLxoAHDAIKJsxY4ZkQpERdcwxx0jhrJtvvtmcdNJJbg9PySB0SCYImA7uij+gRxI1RkaOHCmChu8qlWMpoqkosQLBWcuOOuooaZdDlmuNGjUkcPjaa68Vl1VY12+1zAQMyoK3bNlSMlxWrlwpNWomTZokxbQuvPBC2bnTRkEJHtQY4ZyrmPE+tBKgLgztKCiySXbimjVrJGtRhYySW+wkQgZoY8LGtXLlypLezbXUtWtXE1ZUzAQYLvLhw4dLQaannnpKzJIseKh30jpJ9VaCA2KVPjnqavIu9OoheJ92A7SgoMwC9YFGjx5tSpcu7fbwFB9BmwrCDLDQbN68Web4xo0by8/ef/99c+mll0rYQW5Vk4OEipkQQDM5LDSUvKcJHXUMmDzxz3PBkxWVWwEoxT+ZERRPUzHjPdg4dOzYUb5zWEoRNDxHx+QSJUq4PTzF5xQtWlTmeBqKAtb3rVu3ivupWLFiYvlbuHChCTIqZkIGZc/Hjh0rKX+YtGmKefnll8skS2l0nlf8C0KVomoEhCvuQ1uSdu3aSWo1O+g+ffqIiHnwwQf366KsKOmiXr16ZtGiReK6xJ25fPlyiaEELPX086IScqCIBJwgp2aniyVLlkTat28fOeSQQyIFCxaMNGvWLPLWW29F9u7d6/bQlCTZsWOHnMdBgwa5PZRQ8+WXX0auu+46KYJWokSJyLBhwyLbtm1ze1hKSPnnn38if/75p/x/8ODBsiaecMIJUmjzs88+k597EU3NVpKCEvgU6MIqgx+f3SS+V7KfHnnkEfHHKv7gkEMOkeKKL7/8sttDCSWffvqpWMeqV68uu18ylYiJufPOOyWeSVHcoECBAjld1HFxcm1ecsklZuLEieaUU04RiyH4OblZxUzAwHRI87L8mBBJgcOvT1wN8TXnnXee6du3r3RlJu0Pn6ufL/awQJD30qVLNcA7i1Cdl8XhjDPOEDcfcTG4+mgCSiyToniFAw44QHp7PfrooxKQ/tprr5mrr75afsZ1y7w/btw4321iVcwECAQM9WWoU8F9fn2iqPhzzz1XouPxr2KdYcdJI7SqVatKTQyCyxRvwqJKpoMGAmenuz1NPvm+0Gbk2WeflSJnbdu2lTIJiuJlChUqJEHDWBKB2ElSv7t162ZKliwpVl4ba+N1VMwEiF9//VVEB2qbex6nCkGKVCRlgqZ2DQWa7r77bnP88cdLgS92pGqt8V5mA25CFTOZgeudqtvsbqm8zPeMY/3FF19I9giVuRXFjzRo0ECyW1lDSBQhrZskEaAKMb0BKczqRVTMBAjaxtN48rjjjhNzdzrbyGOtIUKejIwff/xR3E+4ndiRUm79sccek2qNijcgboNsBixrSnrA0jl9+nSJMWM3y2NM9EzyzZs3F/O9ogQBKsffeuut5t1335Uqw/DSSy9JoT5KCVBRfsGCBd7KiIoEnLBlM5GBtGXLlqxkIvEeb775ZuSKK66QLKhDDz000qFDh8gnn3yS8fdWcmfr1q2RQoUKRcaMGeP2UHzP33//HXnuueci1apVk7mkXr16kXnz5nk2A0RRMpmld99990XKlSsn3wWaosLu3bsz8n1IZv3W3kxKWqB1PcFjZEVhDahZs6YoewLLDj30ULeHF0rwd+/atUt2UEryUHjsmWeeMQMHDpQMP44nNTsIkFSUMBOJRMxHH30krTcoyte9e3exUtoeUdRVSgfam0nJOmQ84XoiCBK/KmbK9u3bSxBZ586dzZdffun2EEOZ1UQKpvV5K4mxe/ducZsyIRPIS3Ak2WGvv/56oIRMKpmPSrgpUKCAOeecc0TIAJXH2cBS0ZqSHmeddZa4qLKJ58UMu/xWrVpJICpl+algy8SieBOCH6kozMS/du1a06lTJ/G1ct7q1Kkj2R4sFkrmoW8Lkw7N6JS8IdhxxIgRpnz58nLdIlwI6p02bZrEoAWJdGU+KgrUrVtXsl/ZOBFXySbW1lXi+zNlyhSxroRWzJAlwIRC+hjZA3QDHjZsWFoDW5XMQTO9/v37mx9++MG8+OKLkqqKMMWKg1ly1apVbg8x0GAdI51es5ryNmXjSuJ6JVPvoosuku7jzz33XE7KatDIROajohxyyCGmZcuWsoEiGQXmzZsnLT2KFy8uiQnMRxnZ0EY8DKWWa9eundTfEIhEsJC9/fjjj6EKAPY633zzTeSuu+6KHHXUUXJe6tevH3nxxRcje/bscXtogeTRRx+V4GyCwpV94Zj06dMncsQRR0QOOuigyG233RZZt25dJAwQvD9u3LhIu3bt5BrR1iVKJmEdHjp0aOT000+Xef/VV1+V5zdt2iQB9ukIAPa0mKlSpUqkW7dukauuuipy7LHHRk499dTI448/nuvf9O3bVz589E3FjLfYtWtX5OmnnxaxyvkpXrx4pFevXqFZTLLFTz/9FClQoEBk8uTJbg/FU8ekR48ekcMOOyxSpEiRyB133BHZsGFDJGxkM/NRUSwrV67M2bxeeuml0rusa9eukY8//ni/jKjAiJmDDz5Ybj179ox8+umnkfHjx0cKFy4ceeKJJ+L+jVpm/McXX3wR6dSpU+Twww+XhbdJkyaRmTNnRv766y+3hxYI6tSpE7n44osjYYe5oHPnzjKHFC1aVOaVzZs3uz0sRQl1k+Nu3bqJoGGdrlChQmTp0qXBEzPUyTjnnHP2eY7J6Oyzz074NcJWZ8bPbN++PTJp0qRIzZo15ZyVKlUq0q9fv8j69evdHpqvGTVqlHyXfvvtt0gYWbNmTeTmm2+WY3DkkUdGHnjgAanDoyiKN8DVNHfu3Ejbtm0j//vf/+S5QYMGRR5++OFgdM2mki29gJxUqVJFAkqV4EE9GlJhFy9eLBlr1PUYMmSIKVOmjGnWrJl56623NOsiH1CdlpoplCkPE7TguOGGG0zFihUlIPHhhx+W5pt0CNYkAkXxDgULFpRWCtQqozcU0Kj1wQcfTPg1PC1myGT65ptv9nmODBgWNyXYkAr7+OOPm40bN5oxY8ZImjdZJtT+GDRokNZOSQKyx84+++zQZDXRtZpijWyE6Cc2fPhws27dOslUom+VoijehwKsCJpAiBkaHFJlcMCAAfKhSJVkgevYsaPbQ1OyBFUfb7vtNrN8+XJpann++eebBx54wJQuXVp6htDRNeBFrNMCKZFvvvmm2b59uwkqWPSocUSvsCVLlkj9lDVr1pguXbpIyqiiKP6C6r8JE/E4s2bNilSvXl0CgStXrpxnNlM0GjMTPPCpjhgxIlKpUiU5t9wPHz48x9eq7M/atWvlWL3wwguRoLFw4cLIhRdemHMtPPXUUxo8rigBQHszOdDeTMGFS5eS2ezAcaHQtZiCTfSEotQ21W+VfV139FKhgGEQzv3bb78tcTC0bKhRo4a5//77JT4I/7uiKP5HezMpoQCxQhntqVOnSqNLgsU++OADibU65ZRTzKOPPprxEtp+gl5NtJnYuXOn8bOIefXVV02tWrVM48aNpZIoj3FDtmjRQoWMooQUFTNKIKDhGQGedDcm64lAYWIl6BHSoUMH88knn5iwQ9wM/Yc4Pn5j7969YlEiHoaeU4ULFzZz5swxH3/8sTS5UyucooQbFTNKoMDV1KhRI2luRgr/vffeK4GvZ555pnR1nThxoizoYYQUZRp++imriZRyGthVq1ZNMpRKlChhFi5cKO7FCy+8UEWMoiiCihklsGCV6d27t6Tl4oqg0dnNN98sz9MVmY7IYbTOUG9mz549xsswPjIXK1WqZNq0aSNCDCsMViUy2hRFUZyomFECz4EHHiiuiNmzZ4uw6dy5s3n55ZclaJT4mqefftrs2rXLhCVuhqC6uXPnGi9CPM/o0aMlUJlAbqxpxMMgRs866yy3h6coikdRMaOECgoukgHz448/mpdeeknqj1AllsJyd911135FGoMGheSwdiDmvMS2bdvM4MGDTbly5cydd94p1UBXrFhhXnjhBQnmVhRFyQ0VM0ooKVSokFgpSO+lqjRtFJ588klTuXJlU79+fQk2/fPPP03QIMaEzz1z5kyJR3GbX3/9VbLQEJmkVl9xxRVyPuy5UBRFSQQVM0roOemkk6QHFOndzz77rPn7778l2JQqwz179hTXVNDiZhARVE92i19++cX06tVLRMzAgQNN69atpVrv+PHjTfny5V0bl6Io/kTFjKL8H6T7XnfddZIp8+WXX0q7hMcee0ziN2h6iTUDoeN3SG9GMLiR1USvLdxIiBhiY2hV8d1335lRo0aJeFQURckPKmYUJQakArPAsvjSyXXr1q3iAilbtqzp16+fWHH87GrCOjN9+nSp35INECy33367xMRMnjzZ9OjRQzpYP/LII5JlpiiKkgoqZvzM7t3GrF37772SEQgQvummmyQt+NNPPzWXXnqpGTZsmFgWEDdvvPFG1gRBOiFuBlcPrQAyCUUMiUfClUfANUIQEUOz0KOPPjqj760oSnhQMeNX5s0zhh3tiSf+e89jJaOcdtpp0gcKaw2tErA2XHzxxVJtmLiPTZs2Gb9AyjNunUxlNeGmw2VHEC9FC8lU4ngRg5RUJ1zFG+jGSfE4Kmb8CBNK8+bks/77mHse60STFYoWLWpuueUWs2zZMvPRRx+ZCy64QDJySO+m0eU777wjPYT84GqiUvI///yTttelbQTNHqk0TJ+ssWPHmrVr15o77rjDHHrooWl7HyWL6MZJ8QEqZvzIxo20E6Xr3r+PuecxzytZFQQ0PJwyZYpYa3A/YZGgRgoWieHDh5v//e9/xqsgZn766Sfz4YcfpvxaixYtEisVbSOorExczOrVqyXAl8BqxafoxknxCSpm/EjJksbQDt32peGexzyvuMKRRx4pjS2/+uor6R10xhlniEvl+OOPl7RjrBRes9ace+655rjjjst3VhOfBysUdXmopEwvrOeee86sXLlS4oyo5aP4HN04KT5BxYwfYac7bRr+jn8fc89j3QF7wlpD7yAWdTKeHnroIbF81K5dW9on4Hb5/fffjVeacjZr1kzETDJCi999/fXXRcBgheLz4K76/PPPzbXXXivtI5SAoBsnxSeomPErDRoYQ8DpmjX/3vNY8RTHHnuspCBT0XbOnDnSRqBbt27S6LJ9+/Zm6dKlnshqwqKSyFiIrUG0YHW65JJL5DlEDX+LKEIcKQFDN06KT9DZx88woVAtVScWT8Mif+GFF0rmEMIB9xPihowihMGECRPM9u3bXRlbnTp1zDHHHJNrVhOFAqmMTFAvcTa41HAv4TqjmCDWKCXA6MZJ8QEqZhQli2CVoQcRLRLo4s1jMqO479ixo7hqsgkuoXiuJnpTEchbpUoV06pVKykYiICZN2+eqVevnoqYMKEbJ8XjqJhRFBcoWLCguGpmzZol9VdwP1GRlw7RBOY+9dRTZteuXVkZC9YW+iJ99tln8nj37t1m3LhxUuiuXbt2EutDyvVrr70mY1MURfEaBSJeS7FIM3/88YcU6SJI8XAC1xTFo9DFGnFDYT66eePOadOmjVhuMtlBmvelpUCHDh3knqabmzdvlt5UNIOktYOiKIqX128VM4riQajRQiwNbp4tW7ZIYb5bb71VXEIHHXRQ2t+PGjE02NyzZ4+54YYbzL333iuWGUVRFD+s3+pmUhQPQosEmjCS3k2aN3sOLCVUGUZoUFU3nf2TiIUhSBkRRWNNFTKKovgJtcwoik+gGN348ePNk08+aX777TfTqFEjsdZcdtll+a7tsmPHDnP22WdLsO+SJUv0O6IoimdQy4yiBBCyikaOHGk2bNhgnnjiCfmi0weJDt59+/Y1P/74Y1Kvxz6GejdkVhF8rEJGURS/omJGUXzGIYccIoHBVBam2eXll18ufaBIneb/FLLbu3dvnq8zatQo8/zzz0tvqapVq2Zl7IqiKJlAxYyi+JhTTz3VPPbYY9LokntibEj5PvHEE03//v3Nzz//HPPvCPbt3r27ueuuu0yLFi2yPm5FUZR0ojEzihIg+DoT+0JszdSpUyXt+oorrpDYGgrdUY0Y4XP66aeL24oUcO2lpCiKF9HUbAcqZpSwQpDw008/LXVrVqxYIRlKbdu2lf5KCJpPP/3UFCtWzO1hKoqixETFjAMVM0rY4StO6jWi5oUXXjAlSpQwL730kmQxKYqiBGH9VvuyogQceijVrl1bbmQ+Pf744ypkFEUJFBoArCghonDhwhojoyhK4FAxoygh4u+//5Yml4qiKEFCxYyihAjqz6hlRlGUoOErMTNo0CDx/3fr1s3toSiKLylfvrypU6eO28NQFEUJp5ixtTNq1Kjh9lAUxbeQmk26tqIoSpDwhZjZvn27uf76682ECRPMkUce6fZwFMW37N692+zatcvtYSiKooRPzHTs2FFKtDds2DDP392zZ4/kpjtviqL8y913321q1arl9jAURVHSiucjAWmER6VS3EyJMHDgQPPAAw9kfFyK4kc0AFhRlCDiacvMjz/+aLp27WqeffZZqY+RCD179pRqgfbGayiK8i+amq0oShDx9Bbtk08+MZs3b5ameM6dJR1/x44dKy6l6In54IMPlpuiKPujlhlFUYKIp2e1Bg0amC+++GKf52666SZTuXJlc8899+gOU1GSRC0ziqIEEU+LmaJFi5rq1avv89yhhx5qjj766P2eVxQlb0aNGiWCRlEUJUh4WswoipJe6ECrKIoSNHwnZhYsWJDU70ciEbnXFG1FMWbkyJFSZ4ZAeUVRFC9j1227judGgUgiv+Vj1q9fb0qXLu32MBRFURRFyQdkJZcqVSrcYuaff/4xGzdulPgb+jolqwoRQhzIww8/PGNjDDJ6DFNDj1/q6DFMDT1+qaPHMH8gT7Zt22ZKlixpDjjggGC5mZKFA5CXossLLj69AFNDj2Fq6PFLHT2GqaHHL3X0GGYuzs/TRfMURVEURVHyQsWMoiiKoii+RsVMLlBJuG/fvlpROAX0GKaGHr/U0WOYGnr8UkePYeYJfACwoiiKoijBRi0ziqIoiqL4GhUziqIoiqL4GhUziqIoiqL4GhUziqIoiqL4GhUzMdi7d6/p3bu3KVeunClSpIg58cQTzUMPPZRQf4gw8u6775rLLrtMqjRSZXnGjBn7/Jzj1qdPH3PcccfJ8WzYsKH59ttvXRuv347hX3/9Ze655x5z8sknS9d4fueGG26QytZK4tehk1tvvVV+h15VSuLHb+XKlebyyy+XQmZcizVr1jQ//PCDK+P14zHcvn276dSpkxRyZS6sWrWq+e9//+vaeIOEipkYPPLII+axxx4zY8eOlS8vjwcPHmzGjBnj9tA8yY4dO8wpp5xixo0bF/PnHLvRo0fLl/bjjz+WSbBx48Zm9+7dWR+rH4/hzp07zaeffioCm/tp06aZb775RhYVJfHr0DJ9+nTz0UcfyYKjJH781qxZY2rXrm0qV64sDX8///xzuSYLFy6c9bH69Rjeeeed5s033zTPPPOMrC3dunUTcfPqq69mfayBg9RsZV8uueSSSNu2bfd5rnnz5pHrr7/etTH5BS6p6dOn5zz+559/IiVKlIgMGTIk57nffvstcvDBB0emTp3q0ij9dQxjsXjxYvm977//PmvjCsIxXL9+feT444+PfPnll5EyZcpERowY4cr4/Hj8rr766kirVq1cG1MQjmG1atUiDz744D7PnX766ZH77rsvy6MLHmqZicG5555r5s2bZ1atWiWPP/vsM/P++++bJk2auD0037Fu3Trz888/i2vJgom6Vq1a5sMPP3R1bH7m999/FzP2EUcc4fZQfNV0tnXr1qZHjx6mWrVqbg/Hd8futddeMxUrVhSrarFixeQ7nJsrT4m9tmCF2bBhg7jf58+fL+tMo0aN3B6a71ExE4N7773XXHPNNWJOLVSokDnttNPEHHj99de7PTTfgZCB4sWL7/M8j+3PlOTAPUcMzbXXXqtN65IAd/GBBx5ounTp4vZQfMfmzZsl3mPQoEHmoosuMnPmzDHNmjUzzZs3NwsXLnR7eL6BUAXiZIiZOeigg+RY4pI6//zz3R6a7wl81+z88OKLL5pnn33WPPfcc7KDW758uYgZfOxt2rRxe3hKiCEYuGXLlrKrI65LSYxPPvnEjBo1SmKOsGgpyVtmoGnTpuaOO+6Q/5966qlm0aJFEgtXt25dl0foHzFDvBbWmTJlykjAcMeOHWVtcVqvleRRMRMDzNDWOgNkkXz//fdm4MCBKmaSpESJEnK/adMmyWay8JjJUEleyHAtvvPOO2qVSYL33ntPrAsnnHDCPlmLd911l2Q0fffdd66Oz+scc8wxYtXCquCkSpUq4oJX8mbXrl2mV69eEoB+ySWXyHM1atSQzfLQoUNVzKSIupliQPbIAQfse2gKFiyYsztREof0dgQNMUiWP/74Q7KazjnnHFfH5kchQ0r73LlzzdFHH+32kHwFsTJk37Bw2Bu7YTYub731ltvD8zy4REjDJovOCfEeWBiUxL7D3HRtyQxqmYkBdQL69+8vuzjcTMuWLTPDhw83bdu2dXtongRf+urVq/cJ+mWxOOqoo+QY4qJ7+OGHzUknnSTihnROFpIrrrjC1XH75Rhi0brqqqvERTJ79myxKNh4I37OQqPkfR1GC0Di4RDalSpVcmG0/jt+CL+rr75a4jvq1asnKcazZs2SNG0lsWOIO47jSI0ZRCDxRk899ZSsL0qKuJ1O5UX++OOPSNeuXSMnnHBCpHDhwpHy5ctL6tyePXvcHponmT9/vqQhRt/atGmTk57du3fvSPHixSUlu0GDBpFvvvnG7WH75hiuW7cu5s+48XdKYtdhNJqanfzxmzRpUqRChQoyL55yyimRGTNmuDpmvx3Dn376KXLjjTdGSpYsKcewUqVKkWHDhskcqaRGAf5JVRApiqIoiqK4hcbMKIqiKIria1TMKIqiKIria1TMKIqiKIria1TMKIqiKIria1TMKIqiKIria1TMKIqiKIria1TMKIqiKIria1TMKIqiKIria1TMKIqiKIria1TMKIriK+hNde6555rmzZvv8/zvv/9uSpcube677z7XxqYoijtoOwNFUXwH3ZpPPfVUM2HCBHP99dfLczfccIP57LPPzJIlS7T5pqKEDBUziqL4ktGjR5t+/fqZr776yixevNi0aNFChMwpp5zi9tAURckyKmYURfElTF3169c3BQsWNF988YXp3Lmzuf/++90elqIoLqBiRlEU3/L111+bKlWqmJNPPtl8+umn5sADD3R7SIqiuIAGACuK4lsmT55sDjnkELNu3Tqzfv16t4ejKIpLqGVGURRfsmjRIlO3bl0zZ84c8/DDD8tzc+fONQUKFHB7aIqiZBm1zCiK4jt27txpbrzxRnPbbbeZevXqmUmTJkkQ8H//+1+3h6YoiguoZUZRFN/RtWtX8/rrr0sqNm4mGD9+vOnevbsEA5ctW9btISqKkkVUzCiK4isWLlxoGjRoYBYsWGBq1669z88aN25s/v77b3U3KUrIUDGjKIqiKIqv0ZgZRVEURVF8jYoZRVEURVF8jYoZRVEURVF8jYoZRVEURVF8jYoZRVEURVF8jYoZRVEURVF8jYoZRVEURVF8jYoZRVEURVF8jYoZRVEURVF8jYoZRVEURVF8jYoZRVEURVGMn/l/FUtfU2NYB7EAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "R2pVtuNAV_4D"
   },
   "source": [
    "Ok, that was a brief introduction to `pandas` and some simple visualizations. Now let's talk about web a little bit. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "3KVCBVJ7V_4D"
   },
   "source": [
    "## HTML & CSS Basics\n",
    "\n",
    "### HTML review\n",
    "\n",
    "Webpages are written in a standard markup language called HTML (HyperText Markup Language). The basic syntax of HTML consists of elements enclosed within `<` and `>` symbols. Markup tags often come in a pair, the opening tag without `/` and the closing tag with `/`. For instance, when we assign the title of the webpage, we write `<title>This is the title of the page</title>`. You can find tutorials and references from many websites, including [W3Schools](https://www.w3schools.com/). Here is an example of a simple HTML document (from w3schools homepage):\n",
    "\n",
    "```html\n",
    "<!DOCTYPE html>\n",
    "<html>\n",
    "<title>HTML Tutorial</title>\n",
    "<body>\n",
    "\n",
    "<h1>This is a heading</h1>\n",
    "<p>This is a paragraph.</p>\n",
    "\n",
    "</body>\n",
    "</html>\n",
    "```\n",
    "\n",
    "Here is a list of important tags and their descriptions.\n",
    "\n",
    "- `<html>` - Surrounds the entire document.\n",
    "\n",
    "- `<head>` - Contains information about the document. E.g. the title, metadata, scripts to load, stylesheets, etc.\n",
    "\n",
    "- `<title>` - Assigns title to the page. This is what you see in the tab and what you have when the page is bookmarked. \n",
    "\n",
    "- `<body>` - The main part of the document.\n",
    "\n",
    "- `<h1>`, `<h2>`, `<h3>`, ... - Headings (Smaller the number, larger the size).\n",
    "\n",
    "- `<p>` - Paragraph. e.g., `<p>Here is a paragraph</p>`\n",
    "\n",
    "- `<br>` - Line break.\n",
    "\n",
    "- `<em>` - emphasize text.\n",
    "\n",
    "- `<strong>` - Bold font.\n",
    "\n",
    "- `<a>` - Defines a hyperlink and allows you to link out to the other webpages. See [examples](https://www.w3schools.com/html/html_links.asp)\n",
    "\n",
    "- `<img>` - Place an image. See [examples](https://www.w3schools.com/html/html_images.asp)\n",
    "\n",
    "- `<ul>`, `<ol>`, `<li>` - Unordered lists with bullets, ordered lists with numbers and each item in list respectively. See [examples](https://www.w3schools.com/html/html_lists.asp)\n",
    "\n",
    "- `<table>` - Make a table, specifying contents of each cell. See [examples](https://www.w3schools.com/html/html_tables.asp)\n",
    "\n",
    "- `<!-->` - Comments – will not be displayed.\n",
    "\n",
    "- `<span>` - This will mark a certain part of text but will not necessarily change how they look. CSS or Javascript can access them and change how they look or behave. \n",
    "\n",
    "- `<div>` - Similar to `<span>`, but used for a block that contains many elements. \n",
    "\n",
    "### CSS review\n",
    "\n",
    "While HTML specifies the content and structure, it does not say how they should _look_. \n",
    "\n",
    "CSS (Cascading Style Sheets) is the primary language that is used for the look and formatting of a web document. In the context of creating visualization, CSS becomes critical when you create web-based (Javascript-based) visualizations.\n",
    "\n",
    "A CSS stylesheet consists of one or more selectors, properties and values. For example:\n",
    "\n",
    "```css\n",
    "body {   \n",
    "    background-color: white;   \n",
    "    color: steelblue;   \n",
    "}\n",
    "```\n",
    "\t\n",
    "Selectors are the HTML elements to which the specific styles (combination of properties and values) will be applied. In the above example, all text within the `body` tags will be in steelblue.\n",
    "\n",
    "There are three ways to include CSS code in HTML. This is called \"referencing\".\n",
    "\n",
    "Embed CSS in HTML - You can place the CSS code within `style` tags inside the `head` tags. This way you can keep everything within a single HTML file but does make the code lengthy.\n",
    "\n",
    "```html\n",
    "<head>  \t\t\t\n",
    "  <style type=\"text/css\">\n",
    "      .description {\n",
    "      font: 16px times-new-roman;\n",
    "      }\n",
    "      .viz {\n",
    "      font: 10px sans-serif;\n",
    "      } \n",
    "  </style>\n",
    "</head>\n",
    "```\n",
    "Reference an external stylesheet from HTML is a much cleaner way but results in the creation of another file. To do this, you can copy the CSS code into a text file and save it as a `.css` file in the same folder as the HTML file. In the document head in the HTML code, you can then do the following:\n",
    "\n",
    "```html\n",
    "<head>\n",
    "  <link rel=\"stylesheet\" href=\"main.css\">\n",
    "</head>\n",
    "```\n",
    "\t\n",
    "Attach inline styles - You can also directly attach the styles in-line along with the main HTML code in the body. This makes it easy to customize specific elements but makes the code very messy, because the design and content get mixed up.\n",
    "\n",
    "```html\n",
    "<p style=\"color: green; font-size:36px; font-weight:bold;\">Inline styles can be handy sometimes.</p>\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "waBq0KT7V_4E"
   },
   "source": [
    "**Q5: Create a simple HTML page that displays the Voronoi diagram that you saved. Feel free to add more plots, explanations, and any styles. Make sure to check you can run the Python webserver and open the HTML file that you created.**\n",
    "\n",
    "Btw, you can also export Jupyter notebook into various formats. Click `File -> Export Notebook As` and play with it. "
   ]
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    "## Now submit your work\n",
    "\n",
    "Export your notebook to HTML (`lab02_notebook_lastname_firstname.html`). Then also rename your `HTML` (CSS) files from HTML/CSS exercise in the format of `lab02_lastname_firstname`. Upload these files to Canvas. "
   ]
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